{
  "cells": [
    {
      "cell_type": "markdown",
      "source": [
        "# Decision Trees for Regression Part 2"
      ],
      "metadata": {
        "nteract": {
          "transient": {
            "deleting": false
          }
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "import numpy as np\n",
        "import matplotlib.pyplot as plt\n",
        "import pandas as pd\n",
        "\n",
        "import warnings\n",
        "warnings.filterwarnings(\"ignore\")\n",
        "\n",
        "# yahoo finance is used to fetch data \n",
        "import yfinance as yf\n",
        "yf.pdr_override()"
      ],
      "outputs": [],
      "execution_count": 1,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:25:29.224Z",
          "iopub.execute_input": "2021-09-05T20:25:29.228Z",
          "iopub.status.idle": "2021-09-05T20:25:30.690Z",
          "shell.execute_reply": "2021-09-05T20:25:30.708Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# input\n",
        "symbol = 'AMD'\n",
        "start = '2014-01-01'\n",
        "end = '2018-08-27'\n",
        "\n",
        "# Read data \n",
        "dataset = yf.download(symbol,start,end)\n",
        "\n",
        "# View Columns\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "[*********************100%***********************]  1 of 1 completed\n"
          ]
        },
        {
          "output_type": "execute_result",
          "execution_count": 2,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume\nDate                                                    \n2014-01-02  3.85  3.98  3.84   3.95       3.95  20548400\n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200\n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300\n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100\n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-02</th>\n      <td>3.85</td>\n      <td>3.98</td>\n      <td>3.84</td>\n      <td>3.95</td>\n      <td>3.95</td>\n      <td>20548400</td>\n    </tr>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 2,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:25:30.696Z",
          "iopub.execute_input": "2021-09-05T20:25:30.700Z",
          "iopub.status.idle": "2021-09-05T20:25:31.475Z",
          "shell.execute_reply": "2021-09-05T20:25:31.566Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "# Create more data\n",
        "dataset['Increase_Decrease'] = np.where(dataset['Volume'].shift(-1) > dataset['Volume'],1,0)\n",
        "dataset['Buy_Sell_on_Open'] = np.where(dataset['Open'].shift(-1) > dataset['Open'],1,-1)\n",
        "dataset['Buy_Sell'] = np.where(dataset['Adj Close'].shift(-1) > dataset['Adj Close'],1,-1)\n",
        "dataset['Return'] = dataset['Adj Close'].pct_change()\n",
        "dataset = dataset.dropna()\n",
        "dataset.head()"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 3,
          "data": {
            "text/plain": "            Open  High   Low  Close  Adj Close    Volume  Increase_Decrease  \\\nDate                                                                          \n2014-01-03  3.98  4.00  3.88   4.00       4.00  22887200                  1   \n2014-01-06  4.01  4.18  3.99   4.13       4.13  42398300                  1   \n2014-01-07  4.19  4.25  4.11   4.18       4.18  42932100                  0   \n2014-01-08  4.23  4.26  4.14   4.18       4.18  30678700                  0   \n2014-01-09  4.20  4.23  4.05   4.09       4.09  30667600                  0   \n\n            Buy_Sell_on_Open  Buy_Sell    Return  \nDate                                              \n2014-01-03                 1         1  0.012658  \n2014-01-06                 1         1  0.032500  \n2014-01-07                 1        -1  0.012106  \n2014-01-08                -1        -1  0.000000  \n2014-01-09                -1         1 -0.021531  ",
            "text/html": "<div>\n<style scoped>\n    .dataframe tbody tr th:only-of-type {\n        vertical-align: middle;\n    }\n\n    .dataframe tbody tr th {\n        vertical-align: top;\n    }\n\n    .dataframe thead th {\n        text-align: right;\n    }\n</style>\n<table border=\"1\" class=\"dataframe\">\n  <thead>\n    <tr style=\"text-align: right;\">\n      <th></th>\n      <th>Open</th>\n      <th>High</th>\n      <th>Low</th>\n      <th>Close</th>\n      <th>Adj Close</th>\n      <th>Volume</th>\n      <th>Increase_Decrease</th>\n      <th>Buy_Sell_on_Open</th>\n      <th>Buy_Sell</th>\n      <th>Return</th>\n    </tr>\n    <tr>\n      <th>Date</th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n      <th></th>\n    </tr>\n  </thead>\n  <tbody>\n    <tr>\n      <th>2014-01-03</th>\n      <td>3.98</td>\n      <td>4.00</td>\n      <td>3.88</td>\n      <td>4.00</td>\n      <td>4.00</td>\n      <td>22887200</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.012658</td>\n    </tr>\n    <tr>\n      <th>2014-01-06</th>\n      <td>4.01</td>\n      <td>4.18</td>\n      <td>3.99</td>\n      <td>4.13</td>\n      <td>4.13</td>\n      <td>42398300</td>\n      <td>1</td>\n      <td>1</td>\n      <td>1</td>\n      <td>0.032500</td>\n    </tr>\n    <tr>\n      <th>2014-01-07</th>\n      <td>4.19</td>\n      <td>4.25</td>\n      <td>4.11</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>42932100</td>\n      <td>0</td>\n      <td>1</td>\n      <td>-1</td>\n      <td>0.012106</td>\n    </tr>\n    <tr>\n      <th>2014-01-08</th>\n      <td>4.23</td>\n      <td>4.26</td>\n      <td>4.14</td>\n      <td>4.18</td>\n      <td>4.18</td>\n      <td>30678700</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>-1</td>\n      <td>0.000000</td>\n    </tr>\n    <tr>\n      <th>2014-01-09</th>\n      <td>4.20</td>\n      <td>4.23</td>\n      <td>4.05</td>\n      <td>4.09</td>\n      <td>4.09</td>\n      <td>30667600</td>\n      <td>0</td>\n      <td>-1</td>\n      <td>1</td>\n      <td>-0.021531</td>\n    </tr>\n  </tbody>\n</table>\n</div>"
          },
          "metadata": {}
        }
      ],
      "execution_count": 3,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:25:31.484Z",
          "iopub.execute_input": "2021-09-05T20:25:31.491Z",
          "shell.execute_reply": "2021-09-05T20:25:31.570Z",
          "iopub.status.idle": "2021-09-05T20:25:31.597Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "dataset.shape"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 4,
          "data": {
            "text/plain": "(1170, 10)"
          },
          "metadata": {}
        }
      ],
      "execution_count": 4,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:25:31.603Z",
          "iopub.execute_input": "2021-09-05T20:25:31.606Z",
          "shell.execute_reply": "2021-09-05T20:25:31.580Z",
          "iopub.status.idle": "2021-09-05T20:25:31.613Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "X = dataset.drop(['Adj Close', 'Close'], axis=1)  \n",
        "y = dataset['Adj Close'] "
      ],
      "outputs": [],
      "execution_count": 5,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "shell.execute_reply": "2021-09-05T20:25:31.586Z",
          "iopub.status.busy": "2021-09-05T20:25:31.620Z",
          "iopub.execute_input": "2021-09-05T20:25:31.624Z",
          "iopub.status.idle": "2021-09-05T20:25:31.629Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.model_selection import train_test_split  \n",
        "X_train, X_test, y_train, y_test = train_test_split(X, y, test_size=0.2, random_state=0)"
      ],
      "outputs": [],
      "execution_count": 9,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:26:07.760Z",
          "iopub.execute_input": "2021-09-05T20:26:07.764Z",
          "shell.execute_reply": "2021-09-05T20:26:07.779Z",
          "iopub.status.idle": "2021-09-05T20:26:07.772Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn.tree import DecisionTreeRegressor  \n",
        "regressor = DecisionTreeRegressor()  \n",
        "regressor.fit(X_train, y_train)"
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 10,
          "data": {
            "text/plain": "DecisionTreeRegressor()"
          },
          "metadata": {}
        }
      ],
      "execution_count": 10,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:26:08.799Z",
          "iopub.execute_input": "2021-09-05T20:26:08.804Z",
          "iopub.status.idle": "2021-09-05T20:26:08.815Z",
          "shell.execute_reply": "2021-09-05T20:26:08.826Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "y_pred = regressor.predict(X_test)"
      ],
      "outputs": [],
      "execution_count": 11,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:26:27.470Z",
          "iopub.execute_input": "2021-09-05T20:26:27.480Z",
          "iopub.status.idle": "2021-09-05T20:26:27.488Z",
          "shell.execute_reply": "2021-09-05T20:26:27.495Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "print(\"Original Labels\",y_test)\n",
        "print(\"Labels Predicted\",y_pred)"
      ],
      "outputs": [
        {
          "output_type": "stream",
          "name": "stdout",
          "text": [
            "Original Labels Date\n",
            "2017-08-04    13.120000\n",
            "2017-06-22    14.380000\n",
            "2016-05-17     3.790000\n",
            "2015-04-24     2.300000\n",
            "2015-09-11     2.010000\n",
            "                ...    \n",
            "2017-04-20    13.110000\n",
            "2014-03-11     3.850000\n",
            "2018-07-09    16.610001\n",
            "2018-02-21    11.720000\n",
            "2018-08-20    19.980000\n",
            "Name: Adj Close, Length: 234, dtype: float64\n",
            "Labels Predicted [12.97000027 14.31999969  3.83999991  2.32999992  2.0999999   2.33999991\n",
            " 12.13000011  2.73000002  2.51999998 13.81000042 16.55999947  6.5\n",
            "  2.8599999   7.46999979  2.3900001  16.36000061 13.22000027  3.78999996\n",
            " 11.90999985  4.5999999   3.54999995  2.78999996  4.13999987 20.39999962\n",
            " 12.75        2.3599999   3.03999996 12.31000042 11.43999958  5.11999989\n",
            " 13.57999992 12.47999954 13.34000015  1.83000004 13.18999958 13.31000042\n",
            "  4.01999998  2.1400001   2.18000007 10.55000019  1.98000002  2.36999989\n",
            "  5.94000006  7.11000013  5.40999985  2.71000004 10.89000034  1.82000005\n",
            " 16.5        10.82999992  8.52999973 11.90999985 11.19999981 14.55000019\n",
            " 11.19999981 13.52000046  6.5         6.96000004 10.53999996  2.32999992\n",
            "  1.70000005  3.48000002  2.11999989  1.84000003  3.6500001   3.04999995\n",
            " 12.11999989  2.3499999   2.70000005  1.83000004  7.4000001   2.20000005\n",
            " 12.78999996 10.36999989  2.3599999   3.70000005 13.89999962  1.82000005\n",
            "  2.31999993 11.88000011  2.52999997  6.78000021  2.73000002  2.61999989\n",
            "  4.38000011 12.5        10.43999958  2.25999999  2.02999997 14.25\n",
            "  3.83999991  4.21999979  8.93000031 10.55000019  2.75999999  2.68000007\n",
            " 10.05000019  2.6500001   4.21999979 12.60000038 13.69999981 13.78999996\n",
            " 14.11999989 13.30000019  7.11000013 13.13000011  2.33999991  4.21000004\n",
            "  4.17999983  3.48000002  2.32999992  2.31999993  3.30999994  2.30999994\n",
            "  2.29999995  2.6099999   3.69000006  2.31999993 13.42000008 15.5\n",
            " 11.43999958  6.5        15.5         4.26000023  3.3599999   2.86999989\n",
            "  6.57999992  2.72000003 14.15999985  3.75999999  1.79999995  6.78000021\n",
            "  3.73000002  9.81000042 13.10999966  4.17000008  2.95000005 11.18999958\n",
            "  5.09000015  2.51999998  3.70000005  3.80999994  1.99000001 13.27000046\n",
            "  2.55999994  2.47000003 19.05999947 11.43000031  2.13000011 13.42000008\n",
            "  4.03999996  6.90999985 12.81999969  5.07000017  2.67000008 12.97000027\n",
            "  2.69000006 11.89999962  1.70000005  7.11000013  4.71999979 10.53999996\n",
            "  3.93000007 11.10999966 13.27000046 11.96000004  9.81999969 10.82999992\n",
            " 12.02000046  5.73999977 12.59000015 10.89000034  3.97000003  1.92999995\n",
            " 13.57999992 11.88000011 11.43000031 11.03999996  4.07999992  2.86999989\n",
            "  1.77999997 11.48999977 11.10999966  2.69000006  2.33999991 13.48999977\n",
            "  6.73000002  4.1500001   2.69000006  2.70000005  2.76999998 10.43999958\n",
            "  1.86000001  2.73000002 11.03999996 13.53999996  4.05000019  9.93000031\n",
            " 10.97000027 12.85999966 11.10999966  2.27999997  2.6500001   6.5\n",
            "  4.42000008  2.69000006  2.27999997  1.99000001  3.93000007  2.18000007\n",
            "  2.72000003  3.54999995  3.3599999   1.85000002 13.69999981  2.6099999\n",
            " 19.05999947 15.19999981  2.22000003  2.22000003  6.01999998 16.31999969\n",
            " 10.52000046  2.1500001  11.43999958  9.93000031  2.61999989 13.92000008\n",
            " 11.84000015 13.          3.80999994 16.31999969 11.88000011 19.72999954]\n"
          ]
        }
      ],
      "execution_count": 12,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:28:05.176Z",
          "iopub.execute_input": "2021-09-05T20:28:05.183Z",
          "iopub.status.idle": "2021-09-05T20:28:05.194Z",
          "shell.execute_reply": "2021-09-05T20:28:05.199Z"
        }
      }
    },
    {
      "cell_type": "code",
      "source": [
        "from sklearn import tree\n",
        "\n",
        "tree.plot_tree(regressor) "
      ],
      "outputs": [
        {
          "output_type": "execute_result",
          "execution_count": 14,
          "data": {
            "text/plain": "[Text(219.19855234355387, 211.4, 'X[1] <= 8.355\\nmse = 23.42\\nsamples = 936\\nvalue = 6.938'),\n Text(145.17096350897566, 199.32, 'X[1] <= 4.925\\nmse = 2.132\\nsamples = 587\\nvalue = 3.497'),\n Text(98.63887655347729, 187.24, 'X[1] <= 3.3\\nmse = 0.725\\nsamples = 503\\nvalue = 3.01'),\n Text(56.09310585613859, 175.16, 'X[1] <= 2.465\\nmse = 0.154\\nsamples = 306\\nvalue = 2.39'),\n Text(26.28364925935225, 163.07999999999998, 'X[1] <= 2.095\\nmse = 0.046\\nsamples = 153\\nvalue = 2.052'),\n Text(13.711837810695457, 151.0, 'X[2] <= 1.835\\nmse = 0.012\\nsamples = 75\\nvalue = 1.861'),\n Text(6.745593773537535, 138.92000000000002, 'X[2] <= 1.73\\nmse = 0.004\\nsamples = 40\\nvalue = 1.777'),\n Text(1.9753452171729853, 126.84, 'X[1] <= 1.71\\nmse = 0.002\\nsamples = 16\\nvalue = 1.713'),\n Text(0.6724579462716546, 114.75999999999999, 'X[2] <= 1.63\\nmse = 0.0\\nsamples = 3\\nvalue = 1.65'),\n Text(0.3362289731358273, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 1.62'),\n Text(1.008686919407482, 102.67999999999999, 'X[7] <= -0.012\\nmse = 0.0\\nsamples = 2\\nvalue = 1.665'),\n Text(0.6724579462716546, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.66'),\n Text(1.3449158925433091, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.67'),\n Text(3.278232488074316, 114.75999999999999, 'X[7] <= -0.017\\nmse = 0.001\\nsamples = 13\\nvalue = 1.728'),\n Text(2.353602811950791, 102.67999999999999, 'X[3] <= 15112050.0\\nmse = 0.0\\nsamples = 4\\nvalue = 1.695'),\n Text(2.017373838814964, 90.6, 'X[3] <= 9059950.0\\nmse = 0.0\\nsamples = 3\\nvalue = 1.703'),\n Text(1.6811448656791363, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.71'),\n Text(2.353602811950791, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 1.7'),\n Text(2.6898317850866182, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.67'),\n Text(4.202862164197841, 102.67999999999999, 'X[1] <= 1.76\\nmse = 0.001\\nsamples = 9\\nvalue = 1.742'),\n Text(3.3622897313582727, 90.6, 'X[0] <= 1.725\\nmse = 0.0\\nsamples = 4\\nvalue = 1.718'),\n Text(3.0260607582224455, 78.52000000000001, 'mse = 0.0\\nsamples = 3\\nvalue = 1.72'),\n Text(3.6985187044941, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.71'),\n Text(5.043434597037409, 90.6, 'X[7] <= 0.023\\nmse = 0.0\\nsamples = 5\\nvalue = 1.762'),\n Text(4.370976650765755, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 1.745'),\n Text(4.034747677629928, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.75'),\n Text(4.707205623901582, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.74'),\n Text(5.715892543309064, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 3\\nvalue = 1.773'),\n Text(5.3796635701732365, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.78'),\n Text(6.052121516444891, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 1.77'),\n Text(11.515842329902084, 126.84, 'X[7] <= 0.073\\nmse = 0.002\\nsamples = 24\\nvalue = 1.82'),\n Text(11.179613356766257, 114.75999999999999, 'X[2] <= 1.805\\nmse = 0.001\\nsamples = 23\\nvalue = 1.814'),\n Text(9.078182274667336, 102.67999999999999, 'X[7] <= 0.037\\nmse = 0.0\\nsamples = 16\\nvalue = 1.801'),\n Text(8.74195330153151, 90.6, 'X[3] <= 10196900.0\\nmse = 0.0\\nsamples = 15\\nvalue = 1.798'),\n Text(7.3970374089882, 78.52000000000001, 'X[7] <= 0.014\\nmse = 0.0\\nsamples = 9\\nvalue = 1.804'),\n Text(6.724579462716545, 66.44, 'X[3] <= 6835100.0\\nmse = 0.0\\nsamples = 7\\nvalue = 1.799'),\n Text(6.388350489580718, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.81'),\n Text(7.060808435852373, 54.359999999999985, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 6\\nvalue = 1.797'),\n Text(6.724579462716545, 42.28, 'X[1] <= 1.835\\nmse = 0.0\\nsamples = 3\\nvalue = 1.793'),\n Text(6.388350489580718, 30.19999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 1.8'),\n Text(7.060808435852373, 30.19999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 1.79'),\n Text(7.3970374089882, 42.28, 'mse = 0.0\\nsamples = 3\\nvalue = 1.8'),\n Text(8.069495355259855, 66.44, 'X[7] <= 0.017\\nmse = 0.0\\nsamples = 2\\nvalue = 1.825'),\n Text(7.733266382124027, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.83'),\n Text(8.405724328395682, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.82'),\n Text(10.086869194074819, 78.52000000000001, 'X[3] <= 11990850.0\\nmse = 0.0\\nsamples = 6\\nvalue = 1.788'),\n Text(9.414411247803164, 66.44, 'X[2] <= 1.745\\nmse = 0.0\\nsamples = 2\\nvalue = 1.77'),\n Text(9.078182274667336, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.78'),\n Text(9.75064022093899, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.76'),\n Text(10.759327140346473, 66.44, 'X[0] <= 1.805\\nmse = 0.0\\nsamples = 4\\nvalue = 1.797'),\n Text(10.423098167210645, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.79'),\n Text(11.095556113482301, 54.359999999999985, 'mse = 0.0\\nsamples = 3\\nvalue = 1.8'),\n Text(9.414411247803164, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.85'),\n Text(13.281044438865177, 102.67999999999999, 'X[7] <= 0.011\\nmse = 0.0\\nsamples = 7\\nvalue = 1.844'),\n Text(12.44047200602561, 90.6, 'X[2] <= 1.825\\nmse = 0.0\\nsamples = 5\\nvalue = 1.834'),\n Text(11.768014059753956, 78.52000000000001, 'X[3] <= 14155950.0\\nmse = 0.0\\nsamples = 3\\nvalue = 1.827'),\n Text(11.431785086618127, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 1.83'),\n Text(12.104243032889782, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.82'),\n Text(13.112929952297264, 78.52000000000001, 'X[7] <= -0.011\\nmse = 0.0\\nsamples = 2\\nvalue = 1.845'),\n Text(12.776700979161436, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.85'),\n Text(13.44915892543309, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.84'),\n Text(14.121616871704745, 90.6, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 1.87'),\n Text(13.785387898568919, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.88'),\n Text(14.457845844840573, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.86'),\n Text(11.852071303037912, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 1.96'),\n Text(20.678081847853377, 138.92000000000002, 'X[1] <= 2.005\\nmse = 0.003\\nsamples = 35\\nvalue = 1.957'),\n Text(17.39984935977906, 126.84, 'X[2] <= 1.885\\nmse = 0.001\\nsamples = 18\\nvalue = 1.914'),\n Text(15.970876223951796, 114.75999999999999, 'X[7] <= -0.036\\nmse = 0.0\\nsamples = 12\\nvalue = 1.9'),\n Text(15.634647250815968, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 1.87'),\n Text(16.307105197087623, 102.67999999999999, 'X[1] <= 1.945\\nmse = 0.0\\nsamples = 10\\nvalue = 1.906'),\n Text(15.970876223951796, 90.6, 'X[1] <= 1.925\\nmse = 0.0\\nsamples = 7\\nvalue = 1.896'),\n Text(15.130303791112228, 78.52000000000001, 'X[1] <= 1.91\\nmse = 0.0\\nsamples = 2\\nvalue = 1.88'),\n Text(14.7940748179764, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.89'),\n Text(15.466532764248054, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.87'),\n Text(16.811448656791363, 78.52000000000001, 'X[7] <= 0.013\\nmse = 0.0\\nsamples = 5\\nvalue = 1.902'),\n Text(16.13899071051971, 66.44, 'X[1] <= 1.935\\nmse = 0.0\\nsamples = 3\\nvalue = 1.897'),\n Text(15.802761737383882, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 1.9'),\n Text(16.47521968365554, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.89'),\n Text(17.48390660306302, 66.44, 'X[2] <= 1.855\\nmse = 0.0\\nsamples = 2\\nvalue = 1.91'),\n Text(17.14767762992719, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.9'),\n Text(17.820135576198847, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.92'),\n Text(16.64333417022345, 90.6, 'mse = 0.0\\nsamples = 3\\nvalue = 1.93'),\n Text(18.828822495606328, 114.75999999999999, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 6\\nvalue = 1.942'),\n Text(18.156364549334672, 102.67999999999999, 'X[7] <= 0.003\\nmse = 0.0\\nsamples = 3\\nvalue = 1.927'),\n Text(17.820135576198847, 90.6, 'X[1] <= 1.995\\nmse = 0.0\\nsamples = 2\\nvalue = 1.935'),\n Text(17.48390660306302, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.93'),\n Text(18.156364549334672, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.94'),\n Text(18.4925935224705, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.91'),\n Text(19.50128044187798, 102.67999999999999, 'X[3] <= 9632600.0\\nmse = 0.0\\nsamples = 3\\nvalue = 1.957'),\n Text(19.165051468742156, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 1.96'),\n Text(19.83750941501381, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.95'),\n Text(23.956314335927694, 126.84, 'X[7] <= 0.018\\nmse = 0.001\\nsamples = 17\\nvalue = 2.004'),\n Text(22.359226713532514, 114.75999999999999, 'X[2] <= 1.975\\nmse = 0.0\\nsamples = 11\\nvalue = 1.985'),\n Text(21.182425307557118, 102.67999999999999, 'X[3] <= 6315400.0\\nmse = 0.0\\nsamples = 7\\nvalue = 1.973'),\n Text(20.509967361285465, 90.6, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 1.985'),\n Text(20.173738388149637, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.99'),\n Text(20.84619633442129, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.98'),\n Text(21.854883253828774, 90.6, 'X[2] <= 1.94\\nmse = 0.0\\nsamples = 5\\nvalue = 1.968'),\n Text(21.518654280692946, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.98'),\n Text(22.191112226964602, 78.52000000000001, 'X[1] <= 2.045\\nmse = 0.0\\nsamples = 4\\nvalue = 1.965'),\n Text(21.854883253828774, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 1.96'),\n Text(22.527341200100427, 66.44, 'X[1] <= 2.075\\nmse = 0.0\\nsamples = 3\\nvalue = 1.967'),\n Text(22.191112226964602, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 1.97'),\n Text(22.863570173236255, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 1.96'),\n Text(23.53602811950791, 102.67999999999999, 'X[1] <= 2.055\\nmse = 0.0\\nsamples = 4\\nvalue = 2.005'),\n Text(23.199799146372083, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.03'),\n Text(23.872257092643736, 90.6, 'X[1] <= 2.075\\nmse = 0.0\\nsamples = 3\\nvalue = 1.997'),\n Text(23.53602811950791, 78.52000000000001, 'X[2] <= 2.005\\nmse = 0.0\\nsamples = 2\\nvalue = 2.005'),\n Text(23.199799146372083, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.0'),\n Text(23.872257092643736, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.01'),\n Text(24.208486065779564, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 1.98'),\n Text(25.553401958322873, 114.75999999999999, 'X[2] <= 1.94\\nmse = 0.001\\nsamples = 6\\nvalue = 2.038'),\n Text(24.88094401205122, 102.67999999999999, 'X[0] <= 1.96\\nmse = 0.0\\nsamples = 3\\nvalue = 2.013'),\n Text(24.544715038915392, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 2.01'),\n Text(25.217172985187045, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.02'),\n Text(26.22585990459453, 102.67999999999999, 'X[3] <= 12895450.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.063'),\n Text(25.8896309314587, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 2.07'),\n Text(26.562088877730353, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.05'),\n Text(38.85546070800904, 151.0, 'X[1] <= 2.225\\nmse = 0.01\\nsamples = 78\\nvalue = 2.235'),\n Text(32.57218177253327, 138.92000000000002, 'X[1] <= 2.185\\nmse = 0.003\\nsamples = 26\\nvalue = 2.113'),\n Text(29.84032136580467, 126.84, 'X[7] <= -0.033\\nmse = 0.002\\nsamples = 20\\nvalue = 2.093'),\n Text(27.907004770273666, 114.75999999999999, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 4\\nvalue = 2.032'),\n Text(27.570775797137838, 102.67999999999999, 'X[3] <= 13734800.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.013'),\n Text(27.23454682400201, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 1.99'),\n Text(27.907004770273666, 90.6, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.025'),\n Text(27.570775797137838, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.03'),\n Text(28.24323374340949, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.02'),\n Text(28.24323374340949, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.09'),\n Text(31.773637961335677, 114.75999999999999, 'X[2] <= 2.04\\nmse = 0.001\\nsamples = 16\\nvalue = 2.108'),\n Text(29.924378609088627, 102.67999999999999, 'X[7] <= 0.03\\nmse = 0.0\\nsamples = 7\\nvalue = 2.094'),\n Text(29.251920662816975, 90.6, 'X[1] <= 2.16\\nmse = 0.0\\nsamples = 3\\nvalue = 2.077'),\n Text(28.915691689681147, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.07'),\n Text(29.5881496359528, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.09'),\n Text(30.596836555360284, 90.6, 'X[3] <= 14012500.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.107'),\n Text(30.260607582224456, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.12'),\n Text(30.93306552849611, 78.52000000000001, 'X[7] <= 0.1\\nmse = 0.0\\nsamples = 2\\nvalue = 2.095'),\n Text(30.596836555360284, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.1'),\n Text(31.269294501631936, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.09'),\n Text(33.622897313582726, 102.67999999999999, 'X[7] <= 0.007\\nmse = 0.0\\nsamples = 9\\nvalue = 2.119'),\n Text(32.95043936731108, 90.6, 'X[1] <= 2.175\\nmse = 0.0\\nsamples = 6\\nvalue = 2.11'),\n Text(32.614210394175245, 78.52000000000001, 'X[2] <= 2.085\\nmse = 0.0\\nsamples = 4\\nvalue = 2.1'),\n Text(31.941752447903593, 66.44, 'X[1] <= 2.155\\nmse = 0.0\\nsamples = 2\\nvalue = 2.085'),\n Text(31.605523474767764, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.09'),\n Text(32.27798142103942, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.08'),\n Text(33.2866683404469, 66.44, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.115'),\n Text(32.95043936731108, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.11'),\n Text(33.622897313582726, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.12'),\n Text(33.2866683404469, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.13'),\n Text(34.29535525985438, 90.6, 'X[1] <= 2.17\\nmse = 0.0\\nsamples = 3\\nvalue = 2.137'),\n Text(33.95912628671856, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.14'),\n Text(34.63158423299021, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.13'),\n Text(35.30404217926186, 126.84, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 6\\nvalue = 2.18'),\n Text(34.63158423299021, 114.75999999999999, 'X[1] <= 2.2\\nmse = 0.0\\nsamples = 2\\nvalue = 2.145'),\n Text(34.29535525985438, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.14'),\n Text(34.96781320612604, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.15'),\n Text(35.97650012553352, 114.75999999999999, 'X[7] <= 0.026\\nmse = 0.0\\nsamples = 4\\nvalue = 2.198'),\n Text(35.640271152397695, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.18'),\n Text(36.312729098669344, 102.67999999999999, 'X[1] <= 2.205\\nmse = 0.0\\nsamples = 3\\nvalue = 2.203'),\n Text(35.97650012553352, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 2.2'),\n Text(36.648958071805176, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.21'),\n Text(45.13873964348481, 138.92000000000002, 'X[2] <= 2.235\\nmse = 0.003\\nsamples = 52\\nvalue = 2.296'),\n Text(41.061963344212906, 126.84, 'X[7] <= 0.039\\nmse = 0.002\\nsamples = 16\\nvalue = 2.246'),\n Text(39.42284710017575, 114.75999999999999, 'X[2] <= 2.185\\nmse = 0.001\\nsamples = 13\\nvalue = 2.23'),\n Text(37.657644991212656, 102.67999999999999, 'X[7] <= -0.048\\nmse = 0.001\\nsamples = 5\\nvalue = 2.196'),\n Text(37.321416018076825, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.14'),\n Text(37.99387396434848, 90.6, 'X[0] <= 2.175\\nmse = 0.0\\nsamples = 4\\nvalue = 2.21'),\n Text(37.657644991212656, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.22'),\n Text(38.33010293748431, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.207'),\n Text(37.99387396434848, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.2'),\n Text(38.66633191062014, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.22'),\n Text(41.18804920913884, 102.67999999999999, 'X[7] <= -0.018\\nmse = 0.001\\nsamples = 8\\nvalue = 2.251'),\n Text(40.347476776299274, 90.6, 'X[7] <= -0.028\\nmse = 0.0\\nsamples = 5\\nvalue = 2.238'),\n Text(39.67501883002762, 78.52000000000001, 'X[2] <= 2.22\\nmse = 0.0\\nsamples = 3\\nvalue = 2.253'),\n Text(39.33878985689179, 66.44, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 2\\nvalue = 2.245'),\n Text(39.00256088375596, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.24'),\n Text(39.67501883002762, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.25'),\n Text(40.01124780316345, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.27'),\n Text(41.01993472257093, 78.52000000000001, 'X[0] <= 2.28\\nmse = 0.0\\nsamples = 2\\nvalue = 2.215'),\n Text(40.6837057494351, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.22'),\n Text(41.356163695706755, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.21'),\n Text(42.02862164197841, 90.6, 'X[7] <= 0.003\\nmse = 0.0\\nsamples = 3\\nvalue = 2.273'),\n Text(41.69239266884258, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.26'),\n Text(42.364850615114236, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.28'),\n Text(42.70107958825007, 114.75999999999999, 'X[6] <= 0.0\\nmse = 0.001\\nsamples = 3\\nvalue = 2.313'),\n Text(42.364850615114236, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.28'),\n Text(43.03730856138589, 102.67999999999999, 'X[1] <= 2.345\\nmse = 0.0\\nsamples = 2\\nvalue = 2.33'),\n Text(42.70107958825007, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.32'),\n Text(43.37353753452172, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.34'),\n Text(49.21551594275672, 126.84, 'X[7] <= 0.006\\nmse = 0.001\\nsamples = 36\\nvalue = 2.318'),\n Text(46.483655536028124, 114.75999999999999, 'X[2] <= 2.285\\nmse = 0.001\\nsamples = 22\\nvalue = 2.299'),\n Text(44.382224453929204, 102.67999999999999, 'X[1] <= 2.305\\nmse = 0.0\\nsamples = 11\\nvalue = 2.276'),\n Text(44.04599548079337, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.25'),\n Text(44.71845342706503, 90.6, 'X[0] <= 2.285\\nmse = 0.0\\nsamples = 10\\nvalue = 2.279'),\n Text(44.04599548079337, 78.52000000000001, 'X[0] <= 2.275\\nmse = 0.0\\nsamples = 2\\nvalue = 2.27'),\n Text(43.70976650765755, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.28'),\n Text(44.382224453929204, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.26'),\n Text(45.390911373336685, 78.52000000000001, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 8\\nvalue = 2.281'),\n Text(45.05468240020085, 66.44, 'mse = 0.0\\nsamples = 7\\nvalue = 2.28'),\n Text(45.72714034647251, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.29'),\n Text(48.58508661812704, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 11\\nvalue = 2.321'),\n Text(47.40828521215165, 90.6, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 6\\nvalue = 2.313'),\n Text(46.73582726587999, 78.52000000000001, 'X[3] <= 7510350.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.325'),\n Text(46.399598292744166, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.33'),\n Text(47.07205623901582, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.32'),\n Text(48.0807431584233, 78.52000000000001, 'X[1] <= 2.415\\nmse = 0.0\\nsamples = 4\\nvalue = 2.307'),\n Text(47.74451418528747, 66.44, 'mse = 0.0\\nsamples = 3\\nvalue = 2.31'),\n Text(48.41697213155913, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.3'),\n Text(49.76188802410244, 90.6, 'X[1] <= 2.385\\nmse = 0.0\\nsamples = 5\\nvalue = 2.33'),\n Text(49.42565905096661, 78.52000000000001, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.323'),\n Text(49.089430077830784, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.33'),\n Text(49.76188802410244, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.32'),\n Text(50.098116997238265, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.34'),\n Text(51.94737634948532, 114.75999999999999, 'X[1] <= 2.365\\nmse = 0.001\\nsamples = 14\\nvalue = 2.349'),\n Text(50.77057494350992, 102.67999999999999, 'X[7] <= 0.013\\nmse = 0.0\\nsamples = 8\\nvalue = 2.329'),\n Text(50.43434597037409, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 2.35'),\n Text(51.106803916645745, 90.6, 'X[7] <= 0.022\\nmse = 0.0\\nsamples = 6\\nvalue = 2.322'),\n Text(50.77057494350992, 78.52000000000001, 'X[1] <= 2.355\\nmse = 0.0\\nsamples = 5\\nvalue = 2.326'),\n Text(50.43434597037409, 66.44, 'mse = 0.0\\nsamples = 3\\nvalue = 2.32'),\n Text(51.106803916645745, 66.44, 'X[7] <= 0.02\\nmse = 0.0\\nsamples = 2\\nvalue = 2.335'),\n Text(50.77057494350992, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.34'),\n Text(51.44303288978158, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.33'),\n Text(51.44303288978158, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.3'),\n Text(53.12417775546071, 102.67999999999999, 'X[7] <= 0.015\\nmse = 0.0\\nsamples = 6\\nvalue = 2.377'),\n Text(52.45171980918906, 90.6, 'X[0] <= 2.355\\nmse = 0.0\\nsamples = 3\\nvalue = 2.363'),\n Text(52.115490836053226, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.36'),\n Text(52.78794878232488, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.37'),\n Text(53.79663570173236, 90.6, 'X[7] <= 0.019\\nmse = 0.0\\nsamples = 3\\nvalue = 2.39'),\n Text(53.46040672859654, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.38'),\n Text(54.132864674868195, 78.52000000000001, 'X[3] <= 18683700.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.395'),\n Text(53.79663570173236, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.39'),\n Text(54.46909364800402, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.4'),\n Text(85.90256245292494, 163.07999999999998, 'X[2] <= 2.755\\nmse = 0.033\\nsamples = 153\\nvalue = 2.728'),\n Text(69.90673173487322, 151.0, 'X[2] <= 2.495\\nmse = 0.012\\nsamples = 113\\nvalue = 2.645'),\n Text(60.62628671855386, 138.92000000000002, 'X[1] <= 2.56\\nmse = 0.004\\nsamples = 28\\nvalue = 2.491'),\n Text(57.28501129801657, 126.84, 'X[7] <= -0.03\\nmse = 0.003\\nsamples = 16\\nvalue = 2.456'),\n Text(55.81400954054733, 114.75999999999999, 'X[0] <= 2.535\\nmse = 0.002\\nsamples = 4\\nvalue = 2.395'),\n Text(55.4777805674115, 102.67999999999999, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 2.37'),\n Text(55.141551594275676, 90.6, 'X[1] <= 2.515\\nmse = 0.0\\nsamples = 2\\nvalue = 2.36'),\n Text(54.805322621139844, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.37'),\n Text(55.4777805674115, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.35'),\n Text(55.81400954054733, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.39'),\n Text(56.150238513683156, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.47'),\n Text(58.756013055485816, 114.75999999999999, 'X[1] <= 2.515\\nmse = 0.001\\nsamples = 12\\nvalue = 2.477'),\n Text(57.15892543309064, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 5\\nvalue = 2.452'),\n Text(56.48646748681898, 90.6, 'X[2] <= 2.345\\nmse = 0.0\\nsamples = 3\\nvalue = 2.463'),\n Text(56.150238513683156, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.45'),\n Text(56.82269645995481, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.47'),\n Text(57.83138337936229, 90.6, 'X[7] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.435'),\n Text(57.49515440622646, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.45'),\n Text(58.16761235249812, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.42'),\n Text(60.353100677880995, 102.67999999999999, 'X[3] <= 21737400.0\\nmse = 0.001\\nsamples = 7\\nvalue = 2.494'),\n Text(59.51252824504143, 90.6, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 5\\nvalue = 2.506'),\n Text(58.840070298769774, 78.52000000000001, 'X[7] <= -0.01\\nmse = 0.0\\nsamples = 2\\nvalue = 2.48'),\n Text(58.50384132563395, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.47'),\n Text(59.1762992719056, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.49'),\n Text(60.18498619131309, 78.52000000000001, 'X[1] <= 2.535\\nmse = 0.0\\nsamples = 3\\nvalue = 2.523'),\n Text(59.848757218177255, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.53'),\n Text(60.52121516444891, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.52'),\n Text(61.19367311072057, 90.6, 'X[0] <= 2.345\\nmse = 0.0\\nsamples = 2\\nvalue = 2.465'),\n Text(60.857444137584736, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.46'),\n Text(61.52990208385639, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.47'),\n Text(63.96756213909114, 126.84, 'X[5] <= 0.0\\nmse = 0.003\\nsamples = 12\\nvalue = 2.538'),\n Text(62.53858900326387, 114.75999999999999, 'X[2] <= 2.475\\nmse = 0.0\\nsamples = 5\\nvalue = 2.49'),\n Text(62.20236003012805, 102.67999999999999, 'X[2] <= 2.455\\nmse = 0.0\\nsamples = 4\\nvalue = 2.483'),\n Text(61.86613105699222, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 2.49'),\n Text(62.53858900326387, 90.6, 'X[3] <= 18663250.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.475'),\n Text(62.20236003012805, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.48'),\n Text(62.874817976399704, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.47'),\n Text(62.874817976399704, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.52'),\n Text(65.3965352749184, 114.75999999999999, 'X[1] <= 2.635\\nmse = 0.001\\nsamples = 7\\nvalue = 2.573'),\n Text(64.55596284207884, 102.67999999999999, 'X[7] <= 0.012\\nmse = 0.001\\nsamples = 5\\nvalue = 2.554'),\n Text(63.883504895807185, 90.6, 'X[0] <= 2.545\\nmse = 0.0\\nsamples = 2\\nvalue = 2.585'),\n Text(63.54727592267135, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.58'),\n Text(64.21973386894301, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.59'),\n Text(65.22842078835049, 90.6, 'X[1] <= 2.58\\nmse = 0.0\\nsamples = 3\\nvalue = 2.533'),\n Text(64.89219181521466, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.55'),\n Text(65.56464976148632, 78.52000000000001, 'X[0] <= 2.465\\nmse = 0.0\\nsamples = 2\\nvalue = 2.525'),\n Text(65.22842078835049, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.53'),\n Text(65.90087873462215, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.52'),\n Text(66.23710770775797, 102.67999999999999, 'X[2] <= 2.465\\nmse = 0.0\\nsamples = 2\\nvalue = 2.62'),\n Text(65.90087873462215, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.61'),\n Text(66.5733366808938, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.63'),\n Text(79.18717675119257, 138.92000000000002, 'X[2] <= 2.665\\nmse = 0.005\\nsamples = 85\\nvalue = 2.696'),\n Text(72.12111473763495, 126.84, 'X[1] <= 2.685\\nmse = 0.003\\nsamples = 53\\nvalue = 2.658'),\n Text(68.25448154657293, 114.75999999999999, 'X[1] <= 2.62\\nmse = 0.001\\nsamples = 18\\nvalue = 2.612'),\n Text(67.58202360030128, 102.67999999999999, 'X[3] <= 13562700.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.56'),\n Text(67.24579462716545, 90.6, 'X[1] <= 2.595\\nmse = 0.0\\nsamples = 2\\nvalue = 2.555'),\n Text(66.90956565402963, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.55'),\n Text(67.58202360030128, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.56'),\n Text(67.91825257343712, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.57'),\n Text(68.9269394928446, 102.67999999999999, 'X[7] <= -0.013\\nmse = 0.001\\nsamples = 15\\nvalue = 2.622'),\n Text(68.59071051970876, 90.6, 'mse = 0.0\\nsamples = 3\\nvalue = 2.58'),\n Text(69.26316846598041, 90.6, 'X[1] <= 2.675\\nmse = 0.0\\nsamples = 12\\nvalue = 2.633'),\n Text(68.59071051970876, 78.52000000000001, 'X[3] <= 8717300.0\\nmse = 0.0\\nsamples = 9\\nvalue = 2.627'),\n Text(68.25448154657293, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.66'),\n Text(68.9269394928446, 66.44, 'X[3] <= 10563250.0\\nmse = 0.0\\nsamples = 8\\nvalue = 2.623'),\n Text(68.59071051970876, 54.359999999999985, 'mse = 0.0\\nsamples = 3\\nvalue = 2.63'),\n Text(69.26316846598041, 54.359999999999985, 'X[1] <= 2.655\\nmse = 0.0\\nsamples = 5\\nvalue = 2.618'),\n Text(68.9269394928446, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 2.61'),\n Text(69.59939743911625, 42.28, 'X[0] <= 2.63\\nmse = 0.0\\nsamples = 3\\nvalue = 2.623'),\n Text(69.26316846598041, 30.19999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 2.62'),\n Text(69.93562641225208, 30.19999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.63'),\n Text(69.93562641225208, 78.52000000000001, 'X[0] <= 2.66\\nmse = 0.0\\nsamples = 3\\nvalue = 2.65'),\n Text(69.59939743911625, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.66'),\n Text(70.27185538538791, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.63'),\n Text(75.98774792869696, 114.75999999999999, 'X[7] <= -0.007\\nmse = 0.002\\nsamples = 35\\nvalue = 2.682'),\n Text(73.71820236003013, 102.67999999999999, 'X[2] <= 2.635\\nmse = 0.001\\nsamples = 13\\nvalue = 2.649'),\n Text(72.45734371077077, 90.6, 'X[7] <= -0.019\\nmse = 0.0\\nsamples = 8\\nvalue = 2.636'),\n Text(71.6167712779312, 78.52000000000001, 'X[7] <= -0.054\\nmse = 0.0\\nsamples = 5\\nvalue = 2.624'),\n Text(70.94431333165956, 66.44, 'X[0] <= 2.77\\nmse = 0.0\\nsamples = 2\\nvalue = 2.635'),\n Text(70.60808435852373, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.64'),\n Text(71.28054230479539, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.63'),\n Text(72.28922922420287, 66.44, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 2.617'),\n Text(71.95300025106704, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 2.62'),\n Text(72.62545819733869, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.61'),\n Text(73.29791614361035, 78.52000000000001, 'X[1] <= 2.765\\nmse = 0.0\\nsamples = 3\\nvalue = 2.657'),\n Text(72.96168717047452, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.65'),\n Text(73.63414511674617, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.67'),\n Text(74.97906100928948, 90.6, 'X[2] <= 2.655\\nmse = 0.0\\nsamples = 5\\nvalue = 2.67'),\n Text(74.64283203615365, 78.52000000000001, 'X[3] <= 13335800.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.665'),\n Text(74.30660306301783, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.66'),\n Text(74.97906100928948, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.67'),\n Text(75.31528998242531, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.69'),\n Text(78.2572934973638, 102.67999999999999, 'X[1] <= 2.77\\nmse = 0.002\\nsamples = 22\\nvalue = 2.701'),\n Text(76.82832036153653, 90.6, 'X[3] <= 35386300.0\\nmse = 0.001\\nsamples = 16\\nvalue = 2.683'),\n Text(76.49209138840071, 78.52000000000001, 'X[1] <= 2.73\\nmse = 0.0\\nsamples = 15\\nvalue = 2.687'),\n Text(75.65151895556114, 66.44, 'X[3] <= 8100250.0\\nmse = 0.0\\nsamples = 9\\nvalue = 2.68'),\n Text(74.97906100928948, 54.359999999999985, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.695'),\n Text(74.64283203615365, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.7'),\n Text(75.31528998242531, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.69'),\n Text(76.3239769018328, 54.359999999999985, 'X[3] <= 13818850.0\\nmse = 0.0\\nsamples = 7\\nvalue = 2.676'),\n Text(75.98774792869696, 42.28, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 5\\nvalue = 2.67'),\n Text(75.65151895556114, 30.19999999999999, 'X[7] <= 0.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.668'),\n Text(75.31528998242531, 18.120000000000005, 'mse = 0.0\\nsamples = 1\\nvalue = 2.66'),\n Text(75.98774792869696, 18.120000000000005, 'mse = 0.0\\nsamples = 3\\nvalue = 2.67'),\n Text(76.3239769018328, 30.19999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.68'),\n Text(76.66020587496863, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 2.69'),\n Text(77.33266382124027, 66.44, 'X[2] <= 2.575\\nmse = 0.0\\nsamples = 6\\nvalue = 2.698'),\n Text(76.99643484810444, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.72'),\n Text(77.6688927943761, 54.359999999999985, 'X[3] <= 20782050.0\\nmse = 0.0\\nsamples = 5\\nvalue = 2.694'),\n Text(77.33266382124027, 42.28, 'X[3] <= 15578400.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.7'),\n Text(76.99643484810444, 30.19999999999999, 'X[0] <= 2.68\\nmse = 0.0\\nsamples = 3\\nvalue = 2.697'),\n Text(76.66020587496863, 18.120000000000005, 'mse = 0.0\\nsamples = 1\\nvalue = 2.69'),\n Text(77.33266382124027, 18.120000000000005, 'mse = 0.0\\nsamples = 2\\nvalue = 2.7'),\n Text(77.6688927943761, 30.19999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.71'),\n Text(78.00512176751192, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.67'),\n Text(77.16454933467236, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.61'),\n Text(79.68626663319107, 90.6, 'X[7] <= 0.036\\nmse = 0.001\\nsamples = 6\\nvalue = 2.752'),\n Text(79.0138086869194, 78.52000000000001, 'X[7] <= 0.024\\nmse = 0.0\\nsamples = 4\\nvalue = 2.738'),\n Text(78.67757971378359, 66.44, 'X[0] <= 2.72\\nmse = 0.0\\nsamples = 2\\nvalue = 2.755'),\n Text(78.34135074064775, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.76'),\n Text(79.0138086869194, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.75'),\n Text(79.35003766005524, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 2.72'),\n Text(80.35872457946272, 78.52000000000001, 'X[2] <= 2.58\\nmse = 0.0\\nsamples = 2\\nvalue = 2.78'),\n Text(80.0224956063269, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.77'),\n Text(80.69495355259855, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.79'),\n Text(86.2532387647502, 126.84, 'X[2] <= 2.695\\nmse = 0.002\\nsamples = 32\\nvalue = 2.759'),\n Text(83.04855636454934, 114.75999999999999, 'X[7] <= 0.028\\nmse = 0.001\\nsamples = 8\\nvalue = 2.718'),\n Text(82.37609841827768, 102.67999999999999, 'X[1] <= 2.8\\nmse = 0.0\\nsamples = 6\\nvalue = 2.702'),\n Text(82.03986944514186, 90.6, 'X[3] <= 15528400.0\\nmse = 0.0\\nsamples = 5\\nvalue = 2.706'),\n Text(81.70364047200603, 78.52000000000001, 'X[3] <= 5043550.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.703'),\n Text(81.3674114988702, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.71'),\n Text(82.03986944514186, 66.44, 'mse = 0.0\\nsamples = 3\\nvalue = 2.7'),\n Text(82.37609841827768, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.72'),\n Text(82.71232739141351, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.68'),\n Text(83.72101431082099, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 2.765'),\n Text(83.38478533768516, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.77'),\n Text(84.05724328395682, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.76'),\n Text(89.45792116495105, 114.75999999999999, 'X[7] <= 0.02\\nmse = 0.001\\nsamples = 24\\nvalue = 2.773'),\n Text(87.12533266382124, 102.67999999999999, 'X[2] <= 2.725\\nmse = 0.001\\nsamples = 20\\nvalue = 2.763'),\n Text(84.72970123022847, 90.6, 'X[7] <= -0.027\\nmse = 0.0\\nsamples = 6\\nvalue = 2.738'),\n Text(84.39347225709265, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.72'),\n Text(85.0659302033643, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.748'),\n Text(84.72970123022847, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.73'),\n Text(85.40215917650013, 66.44, 'X[7] <= -0.016\\nmse = 0.0\\nsamples = 3\\nvalue = 2.753'),\n Text(85.0659302033643, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.76'),\n Text(85.73838814963595, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 2.75'),\n Text(89.52096409741401, 90.6, 'X[0] <= 2.785\\nmse = 0.001\\nsamples = 14\\nvalue = 2.774'),\n Text(87.92387647501883, 78.52000000000001, 'X[3] <= 8433700.0\\nmse = 0.0\\nsamples = 10\\nvalue = 2.762'),\n Text(86.74707506904343, 66.44, 'X[7] <= 0.0\\nmse = 0.0\\nsamples = 4\\nvalue = 2.772'),\n Text(86.41084609590762, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.8'),\n Text(87.08330404217926, 54.359999999999985, 'X[3] <= 6963500.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.763'),\n Text(86.74707506904343, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 2.76'),\n Text(87.4195330153151, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.77'),\n Text(89.10067788099423, 66.44, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 6\\nvalue = 2.755'),\n Text(88.42821993472258, 54.359999999999985, 'X[3] <= 14097000.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.763'),\n Text(88.09199096158675, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 2.76'),\n Text(88.76444890785841, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.77'),\n Text(89.77313582726589, 54.359999999999985, 'X[2] <= 2.735\\nmse = 0.0\\nsamples = 3\\nvalue = 2.747'),\n Text(89.43690685413006, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 2.74'),\n Text(90.1093648004017, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 2.76'),\n Text(91.11805171980919, 78.52000000000001, 'X[1] <= 2.905\\nmse = 0.0\\nsamples = 4\\nvalue = 2.802'),\n Text(90.78182274667337, 66.44, 'X[2] <= 2.74\\nmse = 0.0\\nsamples = 3\\nvalue = 2.793'),\n Text(90.44559377353754, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 2.78'),\n Text(91.11805171980919, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 2.8'),\n Text(91.45428069294502, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 2.83'),\n Text(91.79050966608085, 102.67999999999999, 'X[0] <= 2.74\\nmse = 0.001\\nsamples = 4\\nvalue = 2.825'),\n Text(91.45428069294502, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.87'),\n Text(92.12673863921667, 90.6, 'X[2] <= 2.745\\nmse = 0.0\\nsamples = 3\\nvalue = 2.81'),\n Text(91.79050966608085, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.8'),\n Text(92.4629676123525, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.83'),\n Text(101.89839317097666, 151.0, 'X[1] <= 3.015\\nmse = 0.016\\nsamples = 40\\nvalue = 2.961'),\n Text(97.38031634446398, 138.92000000000002, 'X[1] <= 2.89\\nmse = 0.002\\nsamples = 20\\nvalue = 2.848'),\n Text(95.65714285714286, 126.84, 'X[2] <= 2.785\\nmse = 0.001\\nsamples = 11\\nvalue = 2.817'),\n Text(94.48034145116746, 114.75999999999999, 'X[3] <= 10354350.0\\nmse = 0.0\\nsamples = 6\\nvalue = 2.8'),\n Text(93.80788350489581, 102.67999999999999, 'X[7] <= -0.002\\nmse = 0.0\\nsamples = 4\\nvalue = 2.79'),\n Text(93.47165453175998, 90.6, 'X[3] <= 9179800.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.793'),\n Text(93.13542555862416, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.79'),\n Text(93.80788350489581, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.8'),\n Text(94.14411247803164, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.78'),\n Text(95.15279939743913, 102.67999999999999, 'X[1] <= 2.855\\nmse = 0.0\\nsamples = 2\\nvalue = 2.82'),\n Text(94.8165704243033, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.81'),\n Text(95.48902837057494, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.83'),\n Text(96.83394426311825, 114.75999999999999, 'X[0] <= 2.835\\nmse = 0.0\\nsamples = 5\\nvalue = 2.838'),\n Text(96.49771528998242, 102.67999999999999, 'X[7] <= 0.002\\nmse = 0.0\\nsamples = 4\\nvalue = 2.835'),\n Text(96.1614863168466, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.83'),\n Text(96.83394426311825, 90.6, 'X[3] <= 11423700.0\\nmse = 0.0\\nsamples = 3\\nvalue = 2.837'),\n Text(96.49771528998242, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.84'),\n Text(97.17017323625409, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.83'),\n Text(97.17017323625409, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.85'),\n Text(99.1034898317851, 126.84, 'X[7] <= -0.024\\nmse = 0.001\\nsamples = 9\\nvalue = 2.887'),\n Text(98.17886015566157, 114.75999999999999, 'X[2] <= 2.835\\nmse = 0.0\\nsamples = 2\\nvalue = 2.855'),\n Text(97.84263118252574, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.84'),\n Text(98.5150891287974, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 2.87'),\n Text(100.02811950790861, 114.75999999999999, 'X[1] <= 2.925\\nmse = 0.001\\nsamples = 7\\nvalue = 2.896'),\n Text(99.18754707506905, 102.67999999999999, 'X[2] <= 2.785\\nmse = 0.0\\nsamples = 2\\nvalue = 2.86'),\n Text(98.85131810193322, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.85'),\n Text(99.52377604820488, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.87'),\n Text(100.86869194074818, 102.67999999999999, 'X[7] <= 0.016\\nmse = 0.001\\nsamples = 5\\nvalue = 2.91'),\n Text(100.19623399447653, 90.6, 'X[7] <= -0.002\\nmse = 0.001\\nsamples = 3\\nvalue = 2.9'),\n Text(99.8600050213407, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 2.92'),\n Text(100.53246296761236, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.86'),\n Text(101.54114988701984, 90.6, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 2\\nvalue = 2.925'),\n Text(101.20492091388401, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.93'),\n Text(101.87737886015566, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 2.92'),\n Text(106.41646999748933, 138.92000000000002, 'X[2] <= 3.015\\nmse = 0.004\\nsamples = 20\\nvalue = 3.074'),\n Text(104.39909615867437, 126.84, 'X[2] <= 2.99\\nmse = 0.001\\nsamples = 8\\nvalue = 3.02'),\n Text(103.22229475269897, 114.75999999999999, 'X[2] <= 2.935\\nmse = 0.001\\nsamples = 4\\nvalue = 2.993'),\n Text(102.54983680642732, 102.67999999999999, 'X[0] <= 2.97\\nmse = 0.0\\nsamples = 2\\nvalue = 3.02'),\n Text(102.21360783329149, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.0'),\n Text(102.88606577956315, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.04'),\n Text(103.89475269897063, 102.67999999999999, 'X[0] <= 3.065\\nmse = 0.0\\nsamples = 2\\nvalue = 2.965'),\n Text(103.5585237258348, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.95'),\n Text(104.23098167210645, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 2.98'),\n Text(105.57589756464976, 114.75999999999999, 'X[7] <= 0.005\\nmse = 0.0\\nsamples = 4\\nvalue = 3.047'),\n Text(105.23966859151393, 102.67999999999999, 'X[2] <= 3.005\\nmse = 0.0\\nsamples = 3\\nvalue = 3.04'),\n Text(104.90343961837812, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.03'),\n Text(105.57589756464976, 90.6, 'X[3] <= 13058600.0\\nmse = 0.0\\nsamples = 2\\nvalue = 3.045'),\n Text(105.23966859151393, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.05'),\n Text(105.9121265377856, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.04'),\n Text(105.9121265377856, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.07'),\n Text(108.43384383630429, 126.84, 'X[1] <= 3.175\\nmse = 0.002\\nsamples = 12\\nvalue = 3.111'),\n Text(107.25704243032891, 114.75999999999999, 'X[0] <= 3.055\\nmse = 0.0\\nsamples = 9\\nvalue = 3.092'),\n Text(106.92081345719308, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 3.06'),\n Text(107.59327140346473, 102.67999999999999, 'X[7] <= 0.003\\nmse = 0.0\\nsamples = 7\\nvalue = 3.101'),\n Text(106.92081345719308, 90.6, 'X[1] <= 3.155\\nmse = 0.0\\nsamples = 4\\nvalue = 3.092'),\n Text(106.58458448405725, 78.52000000000001, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 3.087'),\n Text(106.24835551092141, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 3.08'),\n Text(106.92081345719308, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 3.1'),\n Text(107.25704243032891, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.11'),\n Text(108.26572934973639, 90.6, 'X[1] <= 3.135\\nmse = 0.0\\nsamples = 3\\nvalue = 3.113'),\n Text(107.92950037660056, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.11'),\n Text(108.6019583228722, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.12'),\n Text(109.61064524227969, 114.75999999999999, 'X[3] <= 12632850.0\\nmse = 0.001\\nsamples = 3\\nvalue = 3.167'),\n Text(109.27441626914387, 102.67999999999999, 'X[3] <= 9135800.0\\nmse = 0.0\\nsamples = 2\\nvalue = 3.145'),\n Text(108.93818729600804, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.14'),\n Text(109.61064524227969, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.15'),\n Text(109.94687421541552, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.21'),\n Text(141.18464725081597, 175.16, 'X[2] <= 3.855\\nmse = 0.087\\nsamples = 197\\nvalue = 3.974'),\n Text(119.87613607833292, 163.07999999999998, 'X[1] <= 3.605\\nmse = 0.026\\nsamples = 79\\nvalue = 3.682'),\n Text(112.88887773035401, 151.0, 'X[2] <= 3.39\\nmse = 0.005\\nsamples = 13\\nvalue = 3.391'),\n Text(111.45990459452675, 138.92000000000002, 'X[1] <= 3.405\\nmse = 0.002\\nsamples = 9\\nvalue = 3.351'),\n Text(110.61933216168717, 126.84, 'X[1] <= 3.375\\nmse = 0.0\\nsamples = 3\\nvalue = 3.3'),\n Text(110.28310318855135, 114.75999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 3.31'),\n Text(110.955561134823, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.28'),\n Text(112.30047702736631, 126.84, 'X[7] <= -0.004\\nmse = 0.001\\nsamples = 6\\nvalue = 3.377'),\n Text(111.62801908109466, 114.75999999999999, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 2\\nvalue = 3.345'),\n Text(111.29179010795883, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.36'),\n Text(111.96424805423048, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.33'),\n Text(112.97293497363796, 114.75999999999999, 'X[1] <= 3.435\\nmse = 0.0\\nsamples = 4\\nvalue = 3.393'),\n Text(112.63670600050214, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.37'),\n Text(113.3091639467738, 102.67999999999999, 'X[2] <= 3.3\\nmse = 0.0\\nsamples = 3\\nvalue = 3.4'),\n Text(112.97293497363796, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.39'),\n Text(113.64539291990963, 90.6, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 3.405'),\n Text(113.3091639467738, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.4'),\n Text(113.98162189304544, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.41'),\n Text(114.31785086618127, 138.92000000000002, 'X[0] <= 3.44\\nmse = 0.002\\nsamples = 4\\nvalue = 3.48'),\n Text(113.98162189304544, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 3.54'),\n Text(114.6540798393171, 126.84, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 3.46'),\n Text(114.31785086618127, 114.75999999999999, 'X[0] <= 3.49\\nmse = 0.0\\nsamples = 2\\nvalue = 3.475'),\n Text(113.98162189304544, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.47'),\n Text(114.6540798393171, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.48'),\n Text(114.99030881245292, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.43'),\n Text(126.86339442631183, 151.0, 'X[1] <= 3.855\\nmse = 0.01\\nsamples = 66\\nvalue = 3.74'),\n Text(121.92503138337936, 138.92000000000002, 'X[2] <= 3.62\\nmse = 0.003\\nsamples = 46\\nvalue = 3.685'),\n Text(117.84825508410746, 126.84, 'X[7] <= -0.012\\nmse = 0.002\\nsamples = 18\\nvalue = 3.636'),\n Text(115.66276675872459, 114.75999999999999, 'X[2] <= 3.545\\nmse = 0.001\\nsamples = 7\\nvalue = 3.593'),\n Text(115.32653778558876, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 3.55'),\n Text(115.99899573186042, 102.67999999999999, 'X[3] <= 15670650.0\\nmse = 0.0\\nsamples = 5\\nvalue = 3.61'),\n Text(115.32653778558876, 90.6, 'X[1] <= 3.695\\nmse = 0.0\\nsamples = 2\\nvalue = 3.595'),\n Text(114.99030881245292, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.59'),\n Text(115.66276675872459, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.6'),\n Text(116.67145367813207, 90.6, 'X[0] <= 3.695\\nmse = 0.0\\nsamples = 3\\nvalue = 3.62'),\n Text(116.33522470499624, 78.52000000000001, 'X[1] <= 3.74\\nmse = 0.0\\nsamples = 2\\nvalue = 3.615'),\n Text(115.99899573186042, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 3.62'),\n Text(116.67145367813207, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 3.61'),\n Text(117.0076826512679, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.63'),\n Text(120.03374340949034, 114.75999999999999, 'X[1] <= 3.69\\nmse = 0.001\\nsamples = 11\\nvalue = 3.664'),\n Text(118.68882751694703, 102.67999999999999, 'X[0] <= 3.575\\nmse = 0.0\\nsamples = 5\\nvalue = 3.634'),\n Text(118.01636957067538, 90.6, 'X[3] <= 14964800.0\\nmse = 0.0\\nsamples = 2\\nvalue = 3.615'),\n Text(117.68014059753955, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.6'),\n Text(118.3525985438112, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.63'),\n Text(119.36128546321868, 90.6, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 3\\nvalue = 3.647'),\n Text(119.02505649008286, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.65'),\n Text(119.69751443635451, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.64'),\n Text(121.37865930203365, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.001\\nsamples = 6\\nvalue = 3.688'),\n Text(120.70620135576199, 90.6, 'X[3] <= 20579800.0\\nmse = 0.0\\nsamples = 2\\nvalue = 3.735'),\n Text(120.36997238262617, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.74'),\n Text(121.04243032889782, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.73'),\n Text(122.0511172483053, 90.6, 'X[1] <= 3.74\\nmse = 0.0\\nsamples = 4\\nvalue = 3.665'),\n Text(121.71488827516947, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.67'),\n Text(122.38734622144113, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.66'),\n Text(126.00180768265128, 126.84, 'X[1] <= 3.825\\nmse = 0.002\\nsamples = 28\\nvalue = 3.717'),\n Text(124.57283454682401, 114.75999999999999, 'X[2] <= 3.67\\nmse = 0.0\\nsamples = 21\\nvalue = 3.702'),\n Text(123.73226211398443, 102.67999999999999, 'X[0] <= 3.76\\nmse = 0.0\\nsamples = 11\\nvalue = 3.693'),\n Text(123.39603314084862, 90.6, 'X[1] <= 3.765\\nmse = 0.0\\nsamples = 10\\nvalue = 3.695'),\n Text(123.05980416771278, 78.52000000000001, 'X[3] <= 11898000.0\\nmse = 0.0\\nsamples = 9\\nvalue = 3.693'),\n Text(122.38734622144113, 66.44, 'X[2] <= 3.65\\nmse = 0.0\\nsamples = 2\\nvalue = 3.685'),\n Text(122.0511172483053, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.68'),\n Text(122.72357519457695, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.69'),\n Text(123.73226211398443, 66.44, 'X[7] <= 0.001\\nmse = 0.0\\nsamples = 7\\nvalue = 3.696'),\n Text(123.39603314084862, 54.359999999999985, 'mse = 0.0\\nsamples = 3\\nvalue = 3.69'),\n Text(124.06849108712026, 54.359999999999985, 'mse = 0.0\\nsamples = 4\\nvalue = 3.7'),\n Text(123.73226211398443, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.71'),\n Text(124.06849108712026, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.67'),\n Text(125.41340697966358, 102.67999999999999, 'X[7] <= -0.013\\nmse = 0.0\\nsamples = 10\\nvalue = 3.713'),\n Text(125.07717800652775, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 3.69'),\n Text(125.74963595279941, 90.6, 'X[3] <= 29880650.0\\nmse = 0.0\\nsamples = 8\\nvalue = 3.719'),\n Text(125.41340697966358, 78.52000000000001, 'X[2] <= 3.705\\nmse = 0.0\\nsamples = 7\\nvalue = 3.713'),\n Text(125.07717800652775, 66.44, 'X[7] <= 0.003\\nmse = 0.0\\nsamples = 5\\nvalue = 3.706'),\n Text(124.74094903339193, 54.359999999999985, 'mse = 0.0\\nsamples = 3\\nvalue = 3.71'),\n Text(125.41340697966358, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 3.7'),\n Text(125.74963595279941, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 3.73'),\n Text(126.08586492593523, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.76'),\n Text(127.43078081847854, 114.75999999999999, 'X[5] <= 0.0\\nmse = 0.003\\nsamples = 7\\nvalue = 3.76'),\n Text(126.75832287220689, 102.67999999999999, 'X[7] <= -0.035\\nmse = 0.002\\nsamples = 5\\nvalue = 3.74'),\n Text(126.42209389907106, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.65'),\n Text(127.0945518453427, 90.6, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 4\\nvalue = 3.762'),\n Text(126.75832287220689, 78.52000000000001, 'mse = 0.0\\nsamples = 3\\nvalue = 3.76'),\n Text(127.43078081847854, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.77'),\n Text(128.1032387647502, 102.67999999999999, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 2\\nvalue = 3.81'),\n Text(127.76700979161437, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.83'),\n Text(128.43946773788602, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.79'),\n Text(131.8017574692443, 138.92000000000002, 'X[7] <= 0.014\\nmse = 0.003\\nsamples = 20\\nvalue = 3.865'),\n Text(130.45684157670098, 126.84, 'X[3] <= 22197150.0\\nmse = 0.001\\nsamples = 12\\nvalue = 3.827'),\n Text(129.78438363042932, 114.75999999999999, 'X[2] <= 3.845\\nmse = 0.0\\nsamples = 5\\nvalue = 3.854'),\n Text(129.44815465729351, 102.67999999999999, 'X[2] <= 3.795\\nmse = 0.0\\nsamples = 4\\nvalue = 3.85'),\n Text(129.11192568415768, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.84'),\n Text(129.78438363042932, 90.6, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 3.853'),\n Text(129.44815465729351, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.86'),\n Text(130.12061260356515, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.85'),\n Text(130.12061260356515, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.87'),\n Text(131.12929952297264, 114.75999999999999, 'X[2] <= 3.76\\nmse = 0.0\\nsamples = 7\\nvalue = 3.807'),\n Text(130.7930705498368, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.78'),\n Text(131.46552849610848, 102.67999999999999, 'X[1] <= 3.87\\nmse = 0.0\\nsamples = 6\\nvalue = 3.812'),\n Text(131.12929952297264, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.8'),\n Text(131.8017574692443, 90.6, 'X[0] <= 3.855\\nmse = 0.0\\nsamples = 5\\nvalue = 3.814'),\n Text(131.46552849610848, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.82'),\n Text(132.1379864423801, 78.52000000000001, 'mse = 0.0\\nsamples = 3\\nvalue = 3.81'),\n Text(133.1466733617876, 126.84, 'X[7] <= 0.027\\nmse = 0.002\\nsamples = 8\\nvalue = 3.924'),\n Text(132.47421541551594, 114.75999999999999, 'X[3] <= 14250050.0\\nmse = 0.0\\nsamples = 5\\nvalue = 3.892'),\n Text(132.1379864423801, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.87'),\n Text(132.81044438865177, 102.67999999999999, 'X[2] <= 3.74\\nmse = 0.0\\nsamples = 4\\nvalue = 3.898'),\n Text(132.47421541551594, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.88'),\n Text(133.1466733617876, 90.6, 'X[1] <= 3.92\\nmse = 0.0\\nsamples = 3\\nvalue = 3.903'),\n Text(132.81044438865177, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.89'),\n Text(133.48290233492344, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.91'),\n Text(133.81913130805927, 114.75999999999999, 'X[1] <= 4.01\\nmse = 0.0\\nsamples = 3\\nvalue = 3.977'),\n Text(133.48290233492344, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 3.99'),\n Text(134.15536028119507, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 3.95'),\n Text(162.49315842329904, 163.07999999999998, 'X[1] <= 4.375\\nmse = 0.033\\nsamples = 118\\nvalue = 4.169'),\n Text(152.71099673612855, 151.0, 'X[1] <= 4.175\\nmse = 0.01\\nsamples = 93\\nvalue = 4.093'),\n Text(145.12483052975145, 138.92000000000002, 'X[2] <= 3.985\\nmse = 0.003\\nsamples = 55\\nvalue = 4.029'),\n Text(139.15676625659052, 126.84, 'X[7] <= -0.0\\nmse = 0.002\\nsamples = 31\\nvalue = 3.994'),\n Text(135.83650514687423, 114.75999999999999, 'X[0] <= 4.0\\nmse = 0.001\\nsamples = 14\\nvalue = 3.966'),\n Text(134.82781822746674, 102.67999999999999, 'X[7] <= -0.008\\nmse = 0.0\\nsamples = 4\\nvalue = 3.928'),\n Text(134.4915892543309, 90.6, 'X[0] <= 3.965\\nmse = 0.0\\nsamples = 3\\nvalue = 3.917'),\n Text(134.15536028119507, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 3.93'),\n Text(134.82781822746674, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 3.91'),\n Text(135.16404720060257, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 3.96'),\n Text(136.8451920662817, 102.67999999999999, 'X[2] <= 3.965\\nmse = 0.001\\nsamples = 10\\nvalue = 3.981'),\n Text(135.83650514687423, 90.6, 'X[3] <= 40685600.0\\nmse = 0.0\\nsamples = 6\\nvalue = 3.97'),\n Text(135.5002761737384, 78.52000000000001, 'X[1] <= 4.05\\nmse = 0.0\\nsamples = 5\\nvalue = 3.962'),\n Text(134.82781822746674, 66.44, 'X[0] <= 4.02\\nmse = 0.0\\nsamples = 2\\nvalue = 3.975'),\n Text(134.4915892543309, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.98'),\n Text(135.16404720060257, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.97'),\n Text(136.17273412001006, 66.44, 'X[7] <= -0.02\\nmse = 0.0\\nsamples = 3\\nvalue = 3.953'),\n Text(135.83650514687423, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 3.96'),\n Text(136.50896309314587, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.94'),\n Text(136.17273412001006, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.01'),\n Text(137.8538789856892, 90.6, 'X[1] <= 4.08\\nmse = 0.0\\nsamples = 4\\nvalue = 3.998'),\n Text(137.18142103941753, 78.52000000000001, 'X[1] <= 4.045\\nmse = 0.0\\nsamples = 2\\nvalue = 3.99'),\n Text(136.8451920662817, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.0'),\n Text(137.51765001255336, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 3.98'),\n Text(138.52633693196083, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.005'),\n Text(138.19010795882502, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.01'),\n Text(138.86256590509666, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.0'),\n Text(142.4770273663068, 114.75999999999999, 'X[1] <= 4.025\\nmse = 0.001\\nsamples = 17\\nvalue = 4.017'),\n Text(140.87993974391162, 102.67999999999999, 'X[1] <= 4.015\\nmse = 0.0\\nsamples = 7\\nvalue = 3.984'),\n Text(140.20748179763999, 90.6, 'X[0] <= 3.965\\nmse = 0.0\\nsamples = 5\\nvalue = 3.976'),\n Text(139.87125282450415, 78.52000000000001, 'X[2] <= 3.92\\nmse = 0.0\\nsamples = 4\\nvalue = 3.97'),\n Text(139.53502385136832, 66.44, 'X[2] <= 3.885\\nmse = 0.0\\nsamples = 3\\nvalue = 3.973'),\n Text(139.1987948782325, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 3.97'),\n Text(139.87125282450415, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 3.98'),\n Text(140.20748179763999, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 3.96'),\n Text(140.54371077077582, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.0'),\n Text(141.55239769018328, 90.6, 'X[0] <= 3.95\\nmse = 0.0\\nsamples = 2\\nvalue = 4.005'),\n Text(141.21616871704745, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.01'),\n Text(141.88862666331912, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.0'),\n Text(144.07411498870198, 102.67999999999999, 'X[1] <= 4.095\\nmse = 0.001\\nsamples = 10\\nvalue = 4.04'),\n Text(142.89731358272658, 90.6, 'X[0] <= 3.94\\nmse = 0.0\\nsamples = 6\\nvalue = 4.028'),\n Text(142.56108460959078, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.04'),\n Text(143.2335425558624, 78.52000000000001, 'X[1] <= 4.075\\nmse = 0.0\\nsamples = 5\\nvalue = 4.026'),\n Text(142.89731358272658, 66.44, 'X[1] <= 4.04\\nmse = 0.0\\nsamples = 3\\nvalue = 4.023'),\n Text(142.56108460959078, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.03'),\n Text(143.2335425558624, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 4.02'),\n Text(143.56977152899825, 66.44, 'mse = 0.0\\nsamples = 2\\nvalue = 4.03'),\n Text(145.25091639467738, 90.6, 'X[0] <= 4.015\\nmse = 0.001\\nsamples = 4\\nvalue = 4.058'),\n Text(144.57845844840574, 78.52000000000001, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.085'),\n Text(144.2422294752699, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.09'),\n Text(144.91468742154154, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.08'),\n Text(145.92337434094904, 78.52000000000001, 'X[2] <= 3.95\\nmse = 0.0\\nsamples = 2\\nvalue = 4.03'),\n Text(145.5871453678132, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.01'),\n Text(146.25960331408487, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.05'),\n Text(151.09289480291238, 126.84, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 24\\nvalue = 4.073'),\n Text(149.20160682902335, 114.75999999999999, 'X[1] <= 4.095\\nmse = 0.001\\nsamples = 14\\nvalue = 4.056'),\n Text(147.60451920662817, 102.67999999999999, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 4\\nvalue = 4.02'),\n Text(146.93206126035653, 90.6, 'X[3] <= 12098450.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.005'),\n Text(146.5958322872207, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.01'),\n Text(147.26829023349234, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.0'),\n Text(148.27697715289983, 90.6, 'X[3] <= 7899750.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.035'),\n Text(147.940748179764, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.04'),\n Text(148.61320612603566, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.03'),\n Text(150.79869445141853, 102.67999999999999, 'X[7] <= 0.002\\nmse = 0.001\\nsamples = 10\\nvalue = 4.071'),\n Text(150.4624654782827, 90.6, 'X[0] <= 4.095\\nmse = 0.0\\nsamples = 9\\nvalue = 4.066'),\n Text(149.2856640723073, 78.52000000000001, 'X[3] <= 14038250.0\\nmse = 0.0\\nsamples = 5\\nvalue = 4.056'),\n Text(148.61320612603566, 66.44, 'X[7] <= -0.002\\nmse = 0.0\\nsamples = 2\\nvalue = 4.07'),\n Text(148.27697715289983, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.06'),\n Text(148.9494350991715, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.08'),\n Text(149.95812201857896, 66.44, 'X[1] <= 4.125\\nmse = 0.0\\nsamples = 3\\nvalue = 4.047'),\n Text(149.62189304544313, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 4.05'),\n Text(150.2943509917148, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.04'),\n Text(151.6392668842581, 78.52000000000001, 'X[7] <= -0.005\\nmse = 0.0\\nsamples = 4\\nvalue = 4.077'),\n Text(151.3030379111223, 66.44, 'X[7] <= -0.01\\nmse = 0.0\\nsamples = 3\\nvalue = 4.083'),\n Text(150.96680893798646, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 4.08'),\n Text(151.6392668842581, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.09'),\n Text(151.97549585739392, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.06'),\n Text(151.13492342455436, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.12'),\n Text(152.98418277680142, 114.75999999999999, 'X[0] <= 4.055\\nmse = 0.0\\nsamples = 10\\nvalue = 4.097'),\n Text(152.31172483052976, 102.67999999999999, 'X[2] <= 4.005\\nmse = 0.0\\nsamples = 4\\nvalue = 4.08'),\n Text(151.97549585739392, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.09'),\n Text(152.6479538036656, 90.6, 'X[0] <= 4.045\\nmse = 0.0\\nsamples = 3\\nvalue = 4.077'),\n Text(152.31172483052976, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 4.08'),\n Text(152.98418277680142, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.07'),\n Text(153.65664072307305, 102.67999999999999, 'X[7] <= 0.0\\nmse = 0.0\\nsamples = 6\\nvalue = 4.108'),\n Text(153.32041174993725, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.08'),\n Text(153.99286969620888, 90.6, 'X[3] <= 31873000.0\\nmse = 0.0\\nsamples = 5\\nvalue = 4.114'),\n Text(153.65664072307305, 78.52000000000001, 'X[1] <= 4.105\\nmse = 0.0\\nsamples = 4\\nvalue = 4.11'),\n Text(153.32041174993725, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.1'),\n Text(153.99286969620888, 66.44, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 3\\nvalue = 4.113'),\n Text(153.65664072307305, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.11'),\n Text(154.32909866934472, 54.359999999999985, 'X[1] <= 4.125\\nmse = 0.0\\nsamples = 2\\nvalue = 4.115'),\n Text(153.99286969620888, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 4.11'),\n Text(154.66532764248055, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 4.12'),\n Text(154.32909866934472, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.13'),\n Text(160.29716294250565, 138.92000000000002, 'X[1] <= 4.27\\nmse = 0.005\\nsamples = 38\\nvalue = 4.186'),\n Text(157.60733115741903, 126.84, 'X[2] <= 4.075\\nmse = 0.002\\nsamples = 24\\nvalue = 4.152'),\n Text(156.01024353502385, 114.75999999999999, 'X[3] <= 34875450.0\\nmse = 0.002\\nsamples = 8\\nvalue = 4.11'),\n Text(155.00155661561638, 102.67999999999999, 'X[2] <= 3.955\\nmse = 0.002\\nsamples = 5\\nvalue = 4.134'),\n Text(154.66532764248055, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.2'),\n Text(155.3377855887522, 90.6, 'X[3] <= 30114050.0\\nmse = 0.001\\nsamples = 4\\nvalue = 4.117'),\n Text(155.00155661561638, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 4.14'),\n Text(155.67401456188804, 78.52000000000001, 'X[0] <= 4.17\\nmse = 0.0\\nsamples = 2\\nvalue = 4.095'),\n Text(155.3377855887522, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.1'),\n Text(156.01024353502385, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.09'),\n Text(157.01893045443134, 102.67999999999999, 'X[3] <= 70230852.0\\nmse = 0.001\\nsamples = 3\\nvalue = 4.07'),\n Text(156.6827014812955, 90.6, 'X[0] <= 4.06\\nmse = 0.0\\nsamples = 2\\nvalue = 4.045'),\n Text(156.34647250815968, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.04'),\n Text(157.01893045443134, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.05'),\n Text(157.35515942756717, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.12'),\n Text(159.2044187798142, 114.75999999999999, 'X[7] <= 0.001\\nmse = 0.001\\nsamples = 16\\nvalue = 4.173'),\n Text(158.36384634697464, 102.67999999999999, 'X[1] <= 4.255\\nmse = 0.0\\nsamples = 8\\nvalue = 4.152'),\n Text(158.0276173738388, 90.6, 'X[3] <= 20208950.0\\nmse = 0.0\\nsamples = 7\\nvalue = 4.149'),\n Text(157.691388400703, 78.52000000000001, 'X[1] <= 4.185\\nmse = 0.0\\nsamples = 6\\nvalue = 4.152'),\n Text(157.35515942756717, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.13'),\n Text(158.0276173738388, 66.44, 'X[0] <= 4.165\\nmse = 0.0\\nsamples = 5\\nvalue = 4.156'),\n Text(157.691388400703, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 4.16'),\n Text(158.36384634697464, 54.359999999999985, 'X[0] <= 4.19\\nmse = 0.0\\nsamples = 3\\nvalue = 4.153'),\n Text(158.0276173738388, 42.28, 'mse = 0.0\\nsamples = 2\\nvalue = 4.15'),\n Text(158.70007532011047, 42.28, 'mse = 0.0\\nsamples = 1\\nvalue = 4.16'),\n Text(158.36384634697464, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.13'),\n Text(158.70007532011047, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.18'),\n Text(160.0449912126538, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 8\\nvalue = 4.194'),\n Text(159.37253326638213, 90.6, 'X[3] <= 8265550.0\\nmse = 0.0\\nsamples = 5\\nvalue = 4.186'),\n Text(159.0363042932463, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.17'),\n Text(159.70876223951797, 78.52000000000001, 'X[3] <= 40369700.0\\nmse = 0.0\\nsamples = 4\\nvalue = 4.19'),\n Text(159.37253326638213, 66.44, 'X[3] <= 23703950.0\\nmse = 0.0\\nsamples = 3\\nvalue = 4.193'),\n Text(159.0363042932463, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 4.19'),\n Text(159.70876223951797, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 4.2'),\n Text(160.0449912126538, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.18'),\n Text(160.71744915892543, 90.6, 'X[3] <= 27631400.0\\nmse = 0.0\\nsamples = 3\\nvalue = 4.207'),\n Text(160.3812201857896, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.19'),\n Text(161.05367813206126, 78.52000000000001, 'X[2] <= 4.155\\nmse = 0.0\\nsamples = 2\\nvalue = 4.215'),\n Text(160.71744915892543, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.21'),\n Text(161.3899071051971, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.22'),\n Text(162.98699472759228, 126.84, 'X[7] <= -0.0\\nmse = 0.003\\nsamples = 14\\nvalue = 4.244'),\n Text(162.06236505146876, 114.75999999999999, 'X[0] <= 4.325\\nmse = 0.002\\nsamples = 4\\nvalue = 4.167'),\n Text(161.72613607833293, 102.67999999999999, 'X[1] <= 4.32\\nmse = 0.0\\nsamples = 3\\nvalue = 4.19'),\n Text(161.3899071051971, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.22'),\n Text(162.06236505146876, 90.6, 'X[3] <= 41703250.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.175'),\n Text(161.72613607833293, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.18'),\n Text(162.39859402460456, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.17'),\n Text(162.39859402460456, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.1'),\n Text(163.9116244037158, 114.75999999999999, 'X[1] <= 4.295\\nmse = 0.0\\nsamples = 10\\nvalue = 4.275'),\n Text(163.07105197087623, 102.67999999999999, 'X[3] <= 13413450.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.24'),\n Text(162.7348229977404, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.25'),\n Text(163.40728094401206, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.23'),\n Text(164.75219683655536, 102.67999999999999, 'X[2] <= 4.185\\nmse = 0.0\\nsamples = 8\\nvalue = 4.284'),\n Text(164.07973889028372, 90.6, 'X[0] <= 4.16\\nmse = 0.0\\nsamples = 4\\nvalue = 4.295'),\n Text(163.7435099171479, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 4.3'),\n Text(164.41596786341955, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 4.29'),\n Text(165.42465478282702, 90.6, 'X[0] <= 4.24\\nmse = 0.0\\nsamples = 4\\nvalue = 4.273'),\n Text(165.0884258096912, 78.52000000000001, 'X[2] <= 4.205\\nmse = 0.0\\nsamples = 2\\nvalue = 4.265'),\n Text(164.75219683655536, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.27'),\n Text(165.42465478282702, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 4.26'),\n Text(165.76088375596285, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 4.28'),\n Text(172.2753201104695, 151.0, 'X[1] <= 4.525\\nmse = 0.017\\nsamples = 25\\nvalue = 4.451'),\n Text(169.29128797388904, 138.92000000000002, 'X[7] <= 0.003\\nmse = 0.006\\nsamples = 14\\nvalue = 4.359'),\n Text(167.7782575947778, 126.84, 'X[2] <= 4.31\\nmse = 0.003\\nsamples = 8\\nvalue = 4.314'),\n Text(166.76957067537032, 114.75999999999999, 'X[2] <= 4.19\\nmse = 0.001\\nsamples = 5\\nvalue = 4.274'),\n Text(166.43334170223451, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 4.24'),\n Text(167.10579964850615, 102.67999999999999, 'X[1] <= 4.455\\nmse = 0.0\\nsamples = 3\\nvalue = 4.297'),\n Text(166.76957067537032, 90.6, 'X[0] <= 4.345\\nmse = 0.0\\nsamples = 2\\nvalue = 4.285'),\n Text(166.43334170223451, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.29'),\n Text(167.10579964850615, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 4.28'),\n Text(167.44202862164198, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.32'),\n Text(168.7869445141853, 114.75999999999999, 'X[2] <= 4.34\\nmse = 0.0\\nsamples = 3\\nvalue = 4.38'),\n Text(168.45071554104948, 102.67999999999999, 'X[1] <= 4.48\\nmse = 0.0\\nsamples = 2\\nvalue = 4.37'),\n Text(168.11448656791364, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.36'),\n Text(168.7869445141853, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.38'),\n Text(169.1231734873211, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.4'),\n Text(170.80431835300027, 126.84, 'X[1] <= 4.42\\nmse = 0.002\\nsamples = 6\\nvalue = 4.42'),\n Text(170.1318604067286, 114.75999999999999, 'X[3] <= 22525650.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.36'),\n Text(169.79563143359277, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.37'),\n Text(170.46808937986444, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.35'),\n Text(171.4767762992719, 114.75999999999999, 'X[1] <= 4.49\\nmse = 0.0\\nsamples = 4\\nvalue = 4.45'),\n Text(171.14054732613607, 102.67999999999999, 'X[3] <= 18884050.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.43'),\n Text(170.80431835300027, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.42'),\n Text(171.4767762992719, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.44'),\n Text(171.81300527240774, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 4.47'),\n Text(175.25935224704997, 138.92000000000002, 'X[7] <= 0.021\\nmse = 0.008\\nsamples = 11\\nvalue = 4.567'),\n Text(173.99849359779063, 126.84, 'X[1] <= 4.675\\nmse = 0.002\\nsamples = 7\\nvalue = 4.513'),\n Text(173.15792116495106, 114.75999999999999, 'X[4] <= 0.5\\nmse = 0.001\\nsamples = 4\\nvalue = 4.48'),\n Text(172.4854632186794, 102.67999999999999, 'X[0] <= 4.485\\nmse = 0.0\\nsamples = 2\\nvalue = 4.515'),\n Text(172.14923424554357, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.52'),\n Text(172.82169219181523, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.51'),\n Text(173.8303791112227, 102.67999999999999, 'X[7] <= -0.021\\nmse = 0.0\\nsamples = 2\\nvalue = 4.445'),\n Text(173.49415013808687, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.43'),\n Text(174.16660808435853, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.46'),\n Text(174.8390660306302, 114.75999999999999, 'X[1] <= 4.755\\nmse = 0.0\\nsamples = 3\\nvalue = 4.557'),\n Text(174.50283705749436, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 4.57'),\n Text(175.17529500376602, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.53'),\n Text(176.52021089630932, 126.84, 'X[7] <= 0.068\\nmse = 0.003\\nsamples = 4\\nvalue = 4.662'),\n Text(176.1839819231735, 114.75999999999999, 'X[1] <= 4.64\\nmse = 0.001\\nsamples = 3\\nvalue = 4.633'),\n Text(175.84775295003766, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.6'),\n Text(176.52021089630932, 102.67999999999999, 'X[3] <= 67447050.0\\nmse = 0.0\\nsamples = 2\\nvalue = 4.65'),\n Text(176.1839819231735, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.66'),\n Text(176.85643986944515, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 4.64'),\n Text(176.85643986944515, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 4.75'),\n Text(191.70305046447402, 187.24, 'X[1] <= 6.24\\nmse = 0.644\\nsamples = 84\\nvalue = 6.412'),\n Text(183.41290484559377, 175.16, 'X[1] <= 5.56\\nmse = 0.123\\nsamples = 23\\nvalue = 5.267'),\n Text(180.89118754707508, 163.07999999999998, 'X[2] <= 5.045\\nmse = 0.026\\nsamples = 18\\nvalue = 5.106'),\n Text(178.36947024855638, 151.0, 'X[1] <= 5.085\\nmse = 0.013\\nsamples = 10\\nvalue = 5.007'),\n Text(177.52889781571682, 138.92000000000002, 'X[0] <= 4.85\\nmse = 0.006\\nsamples = 2\\nvalue = 4.8'),\n Text(177.19266884258099, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 4.88'),\n Text(177.86512678885262, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 4.72'),\n Text(179.21004268139595, 138.92000000000002, 'X[5] <= 0.0\\nmse = 0.002\\nsamples = 8\\nvalue = 5.059'),\n Text(178.53758473512428, 126.84, 'X[6] <= 0.0\\nmse = 0.001\\nsamples = 4\\nvalue = 5.03'),\n Text(178.20135576198845, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.07'),\n Text(178.87381370826012, 114.75999999999999, 'X[2] <= 4.98\\nmse = 0.0\\nsamples = 3\\nvalue = 5.017'),\n Text(178.53758473512428, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 5.02'),\n Text(179.21004268139595, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.01'),\n Text(179.88250062766758, 126.84, 'X[1] <= 5.13\\nmse = 0.001\\nsamples = 4\\nvalue = 5.087'),\n Text(179.54627165453178, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.04'),\n Text(180.2187296008034, 114.75999999999999, 'X[3] <= 28681050.0\\nmse = 0.0\\nsamples = 3\\nvalue = 5.103'),\n Text(179.88250062766758, 102.67999999999999, 'X[2] <= 4.93\\nmse = 0.0\\nsamples = 2\\nvalue = 5.095'),\n Text(179.54627165453178, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 5.1'),\n Text(180.2187296008034, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 5.09'),\n Text(180.55495857393925, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.12'),\n Text(183.41290484559377, 151.0, 'X[1] <= 5.475\\nmse = 0.015\\nsamples = 8\\nvalue = 5.229'),\n Text(182.5723324127542, 138.92000000000002, 'X[4] <= 0.5\\nmse = 0.002\\nsamples = 6\\nvalue = 5.162'),\n Text(181.89987446648257, 126.84, 'X[7] <= 0.009\\nmse = 0.001\\nsamples = 3\\nvalue = 5.133'),\n Text(181.56364549334674, 114.75999999999999, 'X[2] <= 5.095\\nmse = 0.0\\nsamples = 2\\nvalue = 5.115'),\n Text(181.2274165202109, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.1'),\n Text(181.89987446648257, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.13'),\n Text(182.23610343961838, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.17'),\n Text(183.24479035902587, 126.84, 'X[3] <= 21689250.0\\nmse = 0.001\\nsamples = 3\\nvalue = 5.19'),\n Text(182.90856138589004, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.14'),\n Text(183.5810193321617, 114.75999999999999, 'X[0] <= 5.3\\nmse = 0.0\\nsamples = 2\\nvalue = 5.215'),\n Text(183.24479035902587, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.21'),\n Text(183.91724830529753, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 5.22'),\n Text(184.25347727843334, 138.92000000000002, 'X[3] <= 29652100.0\\nmse = 0.0\\nsamples = 2\\nvalue = 5.43'),\n Text(183.91724830529753, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 5.41'),\n Text(184.58970625156917, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 5.45'),\n Text(185.9346221441125, 163.07999999999998, 'X[2] <= 5.705\\nmse = 0.04\\nsamples = 5\\nvalue = 5.85'),\n Text(185.26216419784083, 151.0, 'X[1] <= 5.76\\nmse = 0.013\\nsamples = 2\\nvalue = 5.625'),\n Text(184.925935224705, 138.92000000000002, 'mse = 0.0\\nsamples = 1\\nvalue = 5.51'),\n Text(185.59839317097666, 138.92000000000002, 'mse = 0.0\\nsamples = 1\\nvalue = 5.74'),\n Text(186.60708009038413, 151.0, 'X[3] <= 71132750.0\\nmse = 0.002\\nsamples = 3\\nvalue = 6.0'),\n Text(186.27085111724833, 138.92000000000002, 'X[7] <= 0.024\\nmse = 0.0\\nsamples = 2\\nvalue = 6.03'),\n Text(185.9346221441125, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 6.02'),\n Text(186.60708009038413, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 6.04'),\n Text(186.94330906351996, 138.92000000000002, 'mse = 0.0\\nsamples = 1\\nvalue = 5.94'),\n Text(199.99319608335426, 175.16, 'X[1] <= 7.305\\nmse = 0.16\\nsamples = 61\\nvalue = 6.844'),\n Text(193.2896309314587, 163.07999999999998, 'X[2] <= 6.4\\nmse = 0.056\\nsamples = 47\\nvalue = 6.663'),\n Text(189.12879738890285, 151.0, 'X[3] <= 104571652.0\\nmse = 0.022\\nsamples = 13\\nvalue = 6.351'),\n Text(188.79256841576702, 138.92000000000002, 'X[2] <= 6.23\\nmse = 0.012\\nsamples = 12\\nvalue = 6.322'),\n Text(187.2795380366558, 126.84, 'X[2] <= 6.13\\nmse = 0.003\\nsamples = 7\\nvalue = 6.244'),\n Text(186.60708009038413, 114.75999999999999, 'X[0] <= 6.175\\nmse = 0.0\\nsamples = 2\\nvalue = 6.165'),\n Text(186.27085111724833, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.16'),\n Text(186.94330906351996, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.17'),\n Text(187.95199598292746, 114.75999999999999, 'X[3] <= 81282104.0\\nmse = 0.001\\nsamples = 5\\nvalue = 6.276'),\n Text(187.61576700979163, 102.67999999999999, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 4\\nvalue = 6.288'),\n Text(186.94330906351996, 90.6, 'X[1] <= 6.755\\nmse = 0.0\\nsamples = 2\\nvalue = 6.28'),\n Text(186.60708009038413, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.26'),\n Text(187.2795380366558, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.3'),\n Text(188.2882249560633, 90.6, 'X[3] <= 28628900.0\\nmse = 0.0\\nsamples = 2\\nvalue = 6.295'),\n Text(187.95199598292746, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.3'),\n Text(188.6244539291991, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.29'),\n Text(188.2882249560633, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.23'),\n Text(190.30559879487825, 126.84, 'X[1] <= 6.575\\nmse = 0.005\\nsamples = 5\\nvalue = 6.43'),\n Text(189.6331408486066, 114.75999999999999, 'X[5] <= 0.0\\nmse = 0.005\\nsamples = 3\\nvalue = 6.393'),\n Text(189.29691187547076, 102.67999999999999, 'X[3] <= 32862000.0\\nmse = 0.001\\nsamples = 2\\nvalue = 6.345'),\n Text(188.96068290233492, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.32'),\n Text(189.6331408486066, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.37'),\n Text(189.96936982174242, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.49'),\n Text(190.97805674114989, 114.75999999999999, 'X[3] <= 27460900.0\\nmse = 0.0\\nsamples = 2\\nvalue = 6.485'),\n Text(190.64182776801408, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.47'),\n Text(191.31428571428572, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.5'),\n Text(189.46502636203869, 138.92000000000002, 'mse = 0.0\\nsamples = 1\\nvalue = 6.7'),\n Text(197.45046447401458, 151.0, 'X[1] <= 6.93\\nmse = 0.017\\nsamples = 34\\nvalue = 6.783'),\n Text(194.0881747426563, 138.92000000000002, 'X[2] <= 6.545\\nmse = 0.004\\nsamples = 14\\nvalue = 6.661'),\n Text(192.99543057996485, 126.84, 'X[7] <= 0.042\\nmse = 0.002\\nsamples = 7\\nvalue = 6.619'),\n Text(192.65920160682904, 114.75999999999999, 'X[1] <= 6.725\\nmse = 0.001\\nsamples = 6\\nvalue = 6.607'),\n Text(191.98674366055738, 102.67999999999999, 'X[1] <= 6.655\\nmse = 0.0\\nsamples = 2\\nvalue = 6.57'),\n Text(191.65051468742155, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.58'),\n Text(192.3229726336932, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.56'),\n Text(193.33165955310068, 102.67999999999999, 'X[1] <= 6.745\\nmse = 0.001\\nsamples = 4\\nvalue = 6.625'),\n Text(192.99543057996485, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.67'),\n Text(193.6678885262365, 90.6, 'X[1] <= 6.775\\nmse = 0.0\\nsamples = 3\\nvalue = 6.61'),\n Text(193.33165955310068, 78.52000000000001, 'X[4] <= 0.5\\nmse = 0.0\\nsamples = 2\\nvalue = 6.615'),\n Text(192.99543057996485, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 6.62'),\n Text(193.6678885262365, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 6.61'),\n Text(194.00411749937234, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.6'),\n Text(193.33165955310068, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.69'),\n Text(195.18091890534774, 126.84, 'X[7] <= -0.012\\nmse = 0.002\\nsamples = 7\\nvalue = 6.704'),\n Text(194.34034647250817, 114.75999999999999, 'X[0] <= 6.795\\nmse = 0.0\\nsamples = 2\\nvalue = 6.66'),\n Text(194.00411749937234, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.68'),\n Text(194.676575445644, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.64'),\n Text(196.0214913381873, 114.75999999999999, 'X[6] <= 0.0\\nmse = 0.001\\nsamples = 5\\nvalue = 6.722'),\n Text(195.34903339191564, 102.67999999999999, 'X[3] <= 27567900.0\\nmse = 0.0\\nsamples = 2\\nvalue = 6.69'),\n Text(195.01280441877984, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.68'),\n Text(195.68526236505147, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.7'),\n Text(196.69394928445897, 102.67999999999999, 'X[7] <= 0.007\\nmse = 0.0\\nsamples = 3\\nvalue = 6.743'),\n Text(196.35772031132313, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.77'),\n Text(197.0301782575948, 90.6, 'mse = 0.0\\nsamples = 2\\nvalue = 6.73'),\n Text(200.81275420537284, 138.92000000000002, 'X[0] <= 6.9\\nmse = 0.009\\nsamples = 20\\nvalue = 6.868'),\n Text(199.21566658297766, 126.84, 'X[3] <= 27927500.0\\nmse = 0.003\\nsamples = 12\\nvalue = 6.932'),\n Text(198.3750941501381, 114.75999999999999, 'X[7] <= 0.02\\nmse = 0.003\\nsamples = 4\\nvalue = 6.87'),\n Text(198.03886517700226, 102.67999999999999, 'X[0] <= 6.805\\nmse = 0.0\\nsamples = 3\\nvalue = 6.84'),\n Text(197.70263620386643, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 6.82'),\n Text(198.3750941501381, 90.6, 'X[0] <= 6.85\\nmse = 0.0\\nsamples = 2\\nvalue = 6.85'),\n Text(198.03886517700226, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.84'),\n Text(198.71132312327393, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.86'),\n Text(198.71132312327393, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.96'),\n Text(200.05623901581723, 114.75999999999999, 'X[2] <= 6.59\\nmse = 0.001\\nsamples = 8\\nvalue = 6.962'),\n Text(199.7200100426814, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.01'),\n Text(200.39246798895306, 102.67999999999999, 'X[2] <= 6.725\\nmse = 0.001\\nsamples = 7\\nvalue = 6.956'),\n Text(199.7200100426814, 90.6, 'X[2] <= 6.67\\nmse = 0.0\\nsamples = 2\\nvalue = 6.925'),\n Text(199.3837810695456, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.94'),\n Text(200.05623901581723, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.91'),\n Text(201.06492593522472, 90.6, 'X[1] <= 7.13\\nmse = 0.0\\nsamples = 5\\nvalue = 6.968'),\n Text(200.7286969620889, 78.52000000000001, 'X[1] <= 6.975\\nmse = 0.0\\nsamples = 4\\nvalue = 6.96'),\n Text(200.39246798895306, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 6.95'),\n Text(201.06492593522472, 66.44, 'X[7] <= 0.027\\nmse = 0.0\\nsamples = 3\\nvalue = 6.963'),\n Text(200.7286969620889, 54.359999999999985, 'mse = 0.0\\nsamples = 1\\nvalue = 6.97'),\n Text(201.40115490836055, 54.359999999999985, 'mse = 0.0\\nsamples = 2\\nvalue = 6.96'),\n Text(201.40115490836055, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 7.0'),\n Text(202.40984182776802, 126.84, 'X[3] <= 25515150.0\\nmse = 0.003\\nsamples = 8\\nvalue = 6.773'),\n Text(202.0736128546322, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 6.67'),\n Text(202.74607080090385, 114.75999999999999, 'X[0] <= 6.925\\nmse = 0.001\\nsamples = 7\\nvalue = 6.787'),\n Text(202.40984182776802, 102.67999999999999, 'mse = 0.0\\nsamples = 2\\nvalue = 6.75'),\n Text(203.08229977403968, 102.67999999999999, 'X[3] <= 36209750.0\\nmse = 0.001\\nsamples = 5\\nvalue = 6.802'),\n Text(202.40984182776802, 90.6, 'X[7] <= -0.037\\nmse = 0.0\\nsamples = 3\\nvalue = 6.773'),\n Text(202.0736128546322, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.76'),\n Text(202.74607080090385, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 6.78'),\n Text(203.75475772031132, 90.6, 'X[2] <= 6.73\\nmse = 0.0\\nsamples = 2\\nvalue = 6.845'),\n Text(203.41852874717551, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.85'),\n Text(204.09098669344715, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 6.84'),\n Text(206.69676123524982, 163.07999999999998, 'X[1] <= 7.705\\nmse = 0.036\\nsamples = 14\\nvalue = 7.449'),\n Text(205.26778809942255, 151.0, 'X[3] <= 31399400.0\\nmse = 0.017\\nsamples = 8\\nvalue = 7.31'),\n Text(204.42721566658298, 138.92000000000002, 'X[3] <= 17630050.0\\nmse = 0.002\\nsamples = 5\\nvalue = 7.398'),\n Text(204.09098669344715, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 7.47'),\n Text(204.7634446397188, 126.84, 'X[2] <= 7.265\\nmse = 0.001\\nsamples = 4\\nvalue = 7.38'),\n Text(204.09098669344715, 114.75999999999999, 'X[2] <= 7.17\\nmse = 0.0\\nsamples = 2\\nvalue = 7.345'),\n Text(203.75475772031132, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.35'),\n Text(204.42721566658298, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.34'),\n Text(205.43590258599048, 114.75999999999999, 'X[0] <= 7.595\\nmse = 0.0\\nsamples = 2\\nvalue = 7.415'),\n Text(205.09967361285464, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.4'),\n Text(205.7721315591263, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.43'),\n Text(206.1083605322621, 138.92000000000002, 'X[7] <= -0.026\\nmse = 0.008\\nsamples = 3\\nvalue = 7.163'),\n Text(205.7721315591263, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 7.29'),\n Text(206.44458950539794, 126.84, 'X[1] <= 7.445\\nmse = 0.0\\nsamples = 2\\nvalue = 7.1'),\n Text(206.1083605322621, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.09'),\n Text(206.78081847853377, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 7.11'),\n Text(208.12573437107707, 151.0, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 6\\nvalue = 7.633'),\n Text(207.45327642480544, 138.92000000000002, 'X[3] <= 29217750.0\\nmse = 0.0\\nsamples = 2\\nvalue = 7.585'),\n Text(207.1170474516696, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 7.59'),\n Text(207.78950539794127, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 7.58'),\n Text(208.79819231734874, 138.92000000000002, 'X[1] <= 7.9\\nmse = 0.0\\nsamples = 4\\nvalue = 7.658'),\n Text(208.4619633442129, 126.84, 'mse = 0.0\\nsamples = 3\\nvalue = 7.67'),\n Text(209.13442129048457, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 7.62'),\n Text(293.2261411781321, 199.32, 'X[2] <= 14.225\\nmse = 5.823\\nsamples = 349\\nvalue = 12.725'),\n Text(259.12812343083107, 187.24, 'X[1] <= 12.055\\nmse = 2.073\\nsamples = 297\\nvalue = 11.956'),\n Text(229.92808184785338, 175.16, 'X[2] <= 10.315\\nmse = 0.712\\nsamples = 136\\nvalue = 10.646'),\n Text(215.85900075320112, 163.07999999999998, 'X[2] <= 8.97\\nmse = 0.333\\nsamples = 54\\nvalue = 9.771'),\n Text(210.98368064273163, 151.0, 'X[2] <= 8.39\\nmse = 0.026\\nsamples = 11\\nvalue = 8.744'),\n Text(210.14310820989206, 138.92000000000002, 'X[7] <= -0.021\\nmse = 0.005\\nsamples = 2\\nvalue = 8.46'),\n Text(209.80687923675623, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 8.39'),\n Text(210.47933718302787, 126.84, 'mse = 0.0\\nsamples = 1\\nvalue = 8.53'),\n Text(211.8242530755712, 138.92000000000002, 'X[2] <= 8.705\\nmse = 0.009\\nsamples = 9\\nvalue = 8.807'),\n Text(211.15179512929953, 126.84, 'X[6] <= 0.0\\nmse = 0.002\\nsamples = 5\\nvalue = 8.73'),\n Text(210.8155661561637, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 8.8'),\n Text(211.48802410243536, 114.75999999999999, 'X[2] <= 8.615\\nmse = 0.001\\nsamples = 4\\nvalue = 8.713'),\n Text(211.15179512929953, 102.67999999999999, 'X[7] <= 0.024\\nmse = 0.0\\nsamples = 3\\nvalue = 8.693'),\n Text(210.8155661561637, 90.6, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 8.685'),\n Text(210.47933718302787, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 8.69'),\n Text(211.15179512929953, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 8.68'),\n Text(211.48802410243536, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 8.71'),\n Text(211.8242530755712, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 8.77'),\n Text(212.49671102184283, 126.84, 'X[0] <= 8.795\\nmse = 0.002\\nsamples = 4\\nvalue = 8.902'),\n Text(212.16048204870702, 114.75999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 8.83'),\n Text(212.83293999497866, 114.75999999999999, 'X[3] <= 45671650.0\\nmse = 0.0\\nsamples = 3\\nvalue = 8.927'),\n Text(212.49671102184283, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 8.91'),\n Text(213.1691689681145, 102.67999999999999, 'X[1] <= 9.225\\nmse = 0.0\\nsamples = 2\\nvalue = 8.935'),\n Text(212.83293999497866, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 8.94'),\n Text(213.50539794125032, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 8.93'),\n Text(220.7343208636706, 151.0, 'X[2] <= 9.795\\nmse = 0.072\\nsamples = 43\\nvalue = 10.033'),\n Text(216.78363042932463, 138.92000000000002, 'X[2] <= 9.675\\nmse = 0.024\\nsamples = 15\\nvalue = 9.745'),\n Text(215.01842832036155, 126.84, 'X[0] <= 9.585\\nmse = 0.014\\nsamples = 9\\nvalue = 9.654'),\n Text(214.17785588752199, 114.75999999999999, 'X[7] <= 0.015\\nmse = 0.003\\nsamples = 2\\nvalue = 9.825'),\n Text(213.84162691438615, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 9.88'),\n Text(214.51408486065782, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 9.77'),\n Text(215.85900075320112, 114.75999999999999, 'X[2] <= 9.54\\nmse = 0.007\\nsamples = 7\\nvalue = 9.606'),\n Text(215.18654280692945, 102.67999999999999, 'X[6] <= 0.0\\nmse = 0.001\\nsamples = 5\\nvalue = 9.556'),\n Text(214.85031383379362, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.61'),\n Text(215.52277178006528, 90.6, 'X[7] <= -0.003\\nmse = 0.0\\nsamples = 4\\nvalue = 9.543'),\n Text(215.18654280692945, 78.52000000000001, 'mse = 0.0\\nsamples = 2\\nvalue = 9.53'),\n Text(215.85900075320112, 78.52000000000001, 'X[3] <= 54894450.0\\nmse = 0.0\\nsamples = 2\\nvalue = 9.555'),\n Text(215.52277178006528, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 9.55'),\n Text(216.19522972633695, 66.44, 'mse = 0.0\\nsamples = 1\\nvalue = 9.56'),\n Text(216.53145869947278, 102.67999999999999, 'X[1] <= 10.025\\nmse = 0.0\\nsamples = 2\\nvalue = 9.73'),\n Text(216.19522972633695, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.75'),\n Text(216.86768767260858, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.71'),\n Text(218.54883253828774, 126.84, 'X[7] <= -0.001\\nmse = 0.008\\nsamples = 6\\nvalue = 9.882'),\n Text(217.54014561888025, 114.75999999999999, 'X[6] <= 0.0\\nmse = 0.0\\nsamples = 3\\nvalue = 9.8'),\n Text(217.2039166457444, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 9.77'),\n Text(217.87637459201608, 102.67999999999999, 'X[5] <= 0.0\\nmse = 0.0\\nsamples = 2\\nvalue = 9.815'),\n Text(217.54014561888025, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.81'),\n Text(218.2126035651519, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.82'),\n Text(219.5575194576952, 114.75999999999999, 'X[4] <= 0.5\\nmse = 0.001\\nsamples = 3\\nvalue = 9.963'),\n Text(219.22129048455938, 102.67999999999999, 'X[1] <= 10.045\\nmse = 0.0\\nsamples = 2\\nvalue = 9.99'),\n Text(218.88506151142357, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.98'),\n Text(219.5575194576952, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 10.0'),\n Text(219.89374843083104, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 9.91'),\n Text(224.68501129801658, 138.92000000000002, 'X[1] <= 10.24\\nmse = 0.03\\nsamples = 28\\nvalue = 10.188'),\n Text(221.911122269646, 126.84, 'X[7] <= -0.004\\nmse = 0.005\\nsamples = 11\\nvalue = 10.043'),\n Text(220.90243535023853, 114.75999999999999, 'X[5] <= 0.0\\nmse = 0.001\\nsamples = 3\\nvalue = 9.953'),\n Text(220.5662063771027, 102.67999999999999, 'X[7] <= -0.012\\nmse = 0.0\\nsamples = 2\\nvalue = 9.935'),\n Text(220.22997740396687, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.93'),\n Text(220.90243535023853, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 9.94'),\n Text(221.23866432337434, 102.67999999999999, 'mse = 0.0\\nsamples = 1\\nvalue = 9.99'),\n Text(222.9198091890535, 114.75999999999999, 'X[3] <= 31311000.0\\nmse = 0.002\\nsamples = 8\\nvalue = 10.076'),\n Text(221.911122269646, 102.67999999999999, 'X[3] <= 28940950.0\\nmse = 0.001\\nsamples = 3\\nvalue = 10.127'),\n Text(221.57489329651017, 90.6, 'mse = 0.0\\nsamples = 1\\nvalue = 10.09'),\n Text(222.24735124278183, 90.6, 'X[3] <= 29481600.0\\nmse = 0.0\\nsamples = 2\\nvalue = 10.145'),\n Text(221.911122269646, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 10.13'),\n Text(222.58358021591766, 78.52000000000001, 'mse = 0.0\\nsamples = 1\\nvalue = 10.16'),\n Text(223.92849610846096, 102.67999999999999, 'X[7] <= 0.026\\nmse = 0.0\\nsamples = 5\\nvalue = 10.046'),\n Text(223.59226713532513, 90.6, 'X[3] <= 57220100.0\\nmse = 0.0\\nsamples = 4\\nvalue = 10.038'),\n ...]"
          },
          "metadata": {}
        },
        {
          "output_type": "display_data",
          "data": {
            "text/plain": "<Figure size 432x288 with 1 Axes>",
            "image/png": "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\n"
          },
          "metadata": {
            "needs_background": "light"
          }
        }
      ],
      "execution_count": 14,
      "metadata": {
        "collapsed": true,
        "jupyter": {
          "source_hidden": false,
          "outputs_hidden": false
        },
        "nteract": {
          "transient": {
            "deleting": false
          }
        },
        "execution": {
          "iopub.status.busy": "2021-09-05T20:28:58.378Z",
          "iopub.execute_input": "2021-09-05T20:28:58.382Z",
          "iopub.status.idle": "2021-09-05T20:29:31.135Z",
          "shell.execute_reply": "2021-09-05T20:29:31.218Z"
        }
      }
    }
  ],
  "metadata": {
    "kernel_info": {
      "name": "python3"
    },
    "language_info": {
      "name": "python",
      "version": "3.6.12",
      "mimetype": "text/x-python",
      "codemirror_mode": {
        "name": "ipython",
        "version": 3
      },
      "pygments_lexer": "ipython3",
      "nbconvert_exporter": "python",
      "file_extension": ".py"
    },
    "kernelspec": {
      "argv": [
        "C:/Users/Tin Hang/Anaconda3\\python.exe",
        "-m",
        "ipykernel_launcher",
        "-f",
        "{connection_file}"
      ],
      "display_name": "Python 3",
      "language": "python",
      "name": "python3"
    },
    "nteract": {
      "version": "0.28.0"
    }
  },
  "nbformat": 4,
  "nbformat_minor": 0
}